Nvidia H100 price

Nvidia H100 price

Nvidia H100 Price

Nvidia H100

Introduction to Nvidia H100


The Nvidia H100 Tensor Core GPU marks a significant leap in accelerated computing, setting new standards for performance, scalability, and security in data centers. This advanced GPU, part of Nvidia’s Hopper architecture, is engineered to handle the most demanding data workloads and AI applications. With its ability to connect up to 256 H100 GPUs via the NVLink® Switch System, it’s uniquely poised to accelerate exascale workloads, including trillion-parameter language models, making it a powerhouse for large-scale AI applications and high-performance computing (HPC).

The H100’s PCIe-based NVL model is particularly notable for its ability to manage large language models (LLMs) up to 175 billion parameters. This is achieved through a combination of its Transformer Engine, NVLink, and 80GB HBM3 memory, offering optimal performance and easy scaling across various data center environments. In practical terms, servers equipped with H100 NVL GPUs can deliver up to 12 times the performance of NVIDIA’s previous DGX™ A100 systems for models like GPT-175B, while maintaining low latency, even in power-constrained settings.

A critical aspect of the H100 is its integration with the NVIDIA AI Enterprise software suite, provided with a five-year subscription. This suite simplifies AI adoption and maximizes performance, ensuring that organizations have access to essential AI frameworks and tools. This integration is key for developing AI-driven workflows, such as chatbots, recommendation engines, and vision AI applications.

The H100’s fourth-generation Tensor Cores and Transformer Engine, featuring FP8 precision, offer up to four times faster training for models like GPT-3 (175B), compared to previous generations. Its high-bandwidth interconnects and networking capabilities, coupled with NVIDIA’s software ecosystem, enable efficient scalability from small enterprise systems to extensive, unified GPU clusters.

In the realm of AI inference, the H100 extends NVIDIA’s leadership with advancements that enhance inference speeds by up to 30 times, significantly reducing latency. This advancement is crucial for maintaining accuracy in large language models while optimizing memory usage and overall performance.

The H100 also stands out in its ability to triple the floating-point operations per second (FLOPS) of double-precision Tensor Cores, delivering exceptional computational power for high-performance computing applications. It can achieve one petaflop of throughput for single-precision matrix-multiply operations without any code changes, demonstrating its capability in AI-fused HPC applications.

For data analytics, a primary time-consumer in AI application development, the H100 offers a solution with its accelerated servers. These servers can manage vast datasets with high performance and scalability, thanks to their substantial memory bandwidth and interconnect technologies. This capability is further enhanced by NVIDIA’s comprehensive software ecosystem, including Quantum-2 InfiniBand and GPU-accelerated Spark 3.0.

Additionally, the H100 incorporates second-generation Multi-Instance GPU (MIG) technology, which maximizes GPU utilization by partitioning it into multiple instances. This feature, combined with confidential computing support, makes the H100 ideal for multi-tenant cloud service provider environments, ensuring secure, efficient utilization of resources.

NVIDIA has also integrated Confidential Computing into the Hopper architecture, making the H100 the first accelerator with such capabilities. This feature allows users to protect the confidentiality and integrity of their data and applications while benefiting from the H100’s acceleration capabilities. It creates a trusted execution environment that secures workloads running on the GPU, ensuring data security in compute-intensive applications like AI and HPC.

Moreover, the H100 CNX uniquely combines the power of the H100 with advanced networking capabilities. This convergence is critical for managing GPU-intensive workloads, including distributed AI training in enterprise data centers and 5G processing at the edge, delivering unparalleled performance in these applications.

Finally, the Hopper Tensor Core GPU, including the H100, will power the NVIDIA Grace Hopper CPU+GPU architecture, designed for terabyte-scale accelerated computing. This architecture provides significantly higher performance for large-model AI and HPC applications, demonstrating NVIDIA’s commitment to pushing the boundaries of computing power and efficiency.

Current Pricing Landscape


The current market dynamics of Nvidia’s H100 GPUs offer a fascinating insight into the evolving landscape of high-performance computing and AI infrastructure. Recent trends indicate an increasing availability of these GPUs, as numerous companies have reported receiving thousands of H100 units. This surge in supply is gradually transforming the cost structure associated with H100 GPU computing. For instance, datacenter providers and former Bitcoin mining companies are now able to offer H100 GPU computing services at considerably lower prices compared to large cloud providers, which traditionally charged a premium for VMs accelerated by H100 GPUs.

Amazon’s move to accept reservations for H100 GPUs, ranging from one to 14 days, is a strategic response to anticipated surges in demand. This decision not only underscores the growing interest in these GPUs but also hints at efforts to normalize supply chains, which in turn could make AI more accessible to a broader range of companies.

Nvidia has been carefully managing the distribution of H100 GPUs, focusing on customers with significant AI models, robust infrastructure, and the financial means to invest in advanced computing resources. This selective approach has been crucial in ensuring that these powerful GPUs are utilized effectively across various sectors. Notably, companies like Tesla have been prioritized due to their well-defined AI models and substantial investment capabilities.

In a significant move, datacenter provider Applied Digital acquired 34,000 H100 GPUs, with a plan to deploy a large portion of these by April and additional units subsequently. This purchase not only demonstrates the immense scale at which companies are investing in AI and high-performance computing but also reflects the growing demand for Nvidia’s H100 GPUs in sophisticated data center environments.

The trend towards using H100 GPUs as collateral for securing financing in the tech sector further emphasizes their value beyond mere computing power. Companies like Crusoe Energy and CoreWeave have secured significant funding using H100 GPUs as collateral, indicating the high market value and trust placed in these GPUs.

Looking forward, the market dynamics for Nvidia’s H100 GPUs are poised for interesting shifts. Factors such as the U.S. government’s restrictions on GPU shipments to Chinese companies could potentially free up more manufacturing capacity for H100 chips, potentially impacting their availability and pricing in the U.S. and other markets.

Global Pricing Variations


The Nvidia H100 GPU, heralded as the most powerful in the market, exhibits significant pricing variations across the globe, influenced by factors such as local currencies, regional demand, and supply chain dynamics. In Japan, for example, Nvidia’s official sales partner, GDEP Advance, raised the catalog price of the H100 GPU by 16% in September 2023, setting it at approximately 5.44 million yen ($36,300). This increase reflects not only the high demand for the chip in AI and generative AI development but also the impact of currency fluctuations. The weakening yen, losing about 20% of its value against the US dollar in the past year, has compounded the cost for Japanese companies, who now have to pay more to purchase these GPUs from the US.

In contrast, in the United States, the H100 GPU’s price tends to be more stable, with an average retail price of around $30,000 for an H100 PCIe card. This price, however, can vary significantly based on the purchase volume and packaging. For instance, large-scale purchases by technology companies and educational institutions can lead to noticeable price differences. Such variations are a testament to Nvidia’s robust market positioning, allowing for flexibility in pricing strategies.

Saudi Arabia and the United Arab Emirates (UAE) are also notable players in the H100 market, having purchased thousands of these GPUs. The UAE, which has developed its open-source large language model using Nvidia’s A100 GPUs, is expanding its infrastructure with the H100. This purchase decision underscores the growing global interest in advanced AI and computing capabilities.

The pricing dynamics in different regions are not just a reflection of supply and demand but also an indicator of the strategic importance of advanced computing technologies. In regions like Japan, where the cost of H100 GPUs has surged, it adds a significant burden to companies already grappling with high expenses in developing generative AI products and services. Such disparities in pricing can potentially influence the competitive landscape in the global AI market, as companies in higher-cost regions may find it challenging to keep pace with their counterparts in regions where these critical resources are more affordable.

Market Demand and Supply Dynamics


The Nvidia H100 GPU, a cornerstone of contemporary AI and high-performance computing, has sparked a significant shift in the market dynamics of GPU computing. This transformation is characterized by an increased supply meeting the rising demand from diverse sectors.

A significant number of companies, both large and small, have reported receiving thousands of H100 GPUs in recent months. This surge in availability is reducing the previously long waiting times for accessing these powerful GPUs in the cloud. Datacenter providers and companies transitioning from Bitcoin mining to AI computing are opening new facilities equipped with H100 GPUs. They offer computing power at more competitive rates than larger cloud providers, who traditionally charged a premium for VMs accelerated by H100 GPUs.

Amazon’s initiative to take reservations for H100 GPUs for short-term use reflects a strategic approach to managing future demand surges. This development is crucial in helping companies execute AI strategies that were previously hindered by GPU shortages.

High-profile companies like Tesla have been among the early adopters of the H100. Tesla’s deployment of a large cluster of these GPUs underscores the significant role they play in advancing AI capabilities, particularly in areas like autonomous vehicle development.

Nvidia has adopted a strategic rationing approach for the H100 GPUs, focusing on customers with substantial AI models and infrastructure. This selective distribution has been necessary due to the intense demand and limited supply of these high-end GPUs.

Large orders, such as Applied Digital’s purchase of 34,000 H100 GPUs, highlight the scale at which businesses are investing in AI and computing infrastructure. This trend is set to continue with more companies seeking to enhance their AI capabilities through advanced GPU computing.

Interestingly, diverse sectors are now venturing into AI computing, utilizing H100 GPUs. For instance, Iris Energy, a former cryptocurrency miner, is transforming its operations to focus on generative AI, leveraging the power of H100 GPUs. This shift indicates the broadening application of GPUs beyond traditional computing tasks.

Voltage Park’s acquisition of a large supply of H100 GPUs and its innovative approach to making GPU computing capacity accessible through the FLOP Auction initiative demonstrate the evolving business models around GPU utilization. This model allows for more flexible and market-driven access to GPU resources.

The H100 GPUs are also becoming valuable assets, used as collateral for significant financing deals in the tech sector. This trend reflects the intrinsic value attached to these GPUs in the current market.

Nvidia’s partnership with cloud providers to expand H100 capacity further diversifies the availability of these GPUs. Large cloud service providers like Oracle, Google, and Microsoft have enhanced their services with H100 GPUs, integrating them into their supercomputing and AI offerings. This integration is a testament to the pivotal role of GPUs in driving the next generation of cloud and AI services.

In summary, the market dynamics around Nvidia’s H100 GPUs are characterized by increased supply, diverse applications, innovative business models, and strategic partnerships, all of which are reshaping the landscape of AI and high-performance computing.

Impact of Increasing GPU Capacity on Pricing


The evolving landscape of Nvidia H100 GPU capacity and its implications on pricing dynamics is a multi-faceted narrative in the world of high-performance computing and AI.

The increasing availability of H100 GPUs, as reported by a wide range of companies, signals a shift towards shorter wait times for cloud access to these GPUs. This development is particularly significant as datacenter providers and companies transitioning from cryptocurrency to AI computing are offering H100 GPU computing at costs lower than those of large cloud providers. This democratization of access is reshaping the economic landscape of GPU computing, making it more accessible and affordable.

Amazon’s initiative to take reservations for H100 GPUs indicates a strategic response to anticipated demand surges, reflecting the broader trend of normalizing GPU supply. This strategy is pivotal in enabling companies to actualize their AI ambitions, which were previously hampered by GPU scarcity.

High-profile adoptions, such as Tesla’s activation of a 10,000 H100 GPU cluster, underscore the critical role of these GPUs in cutting-edge AI applications. Such large-scale deployments also reflect the growing demand and reliance on advanced GPU capabilities in sectors like autonomous driving.

Nvidia’s rationing of H100 GPUs due to shortages and prioritizing customers based on their AI model size, infrastructure, and profile illustrates a targeted approach to distribution. This strategy ensures that these powerful GPUs are allocated to applications where they can be most effectively utilized.

Large-scale purchases, such as Applied Digital’s order of 34,000 H100 GPUs, indicate the scale of investment in AI and computing infrastructure, further driving up demand. Such massive deployments also point to the growing importance of GPU computing in high-performance data centers.

The wide-ranging applications of H100 GPUs across various sectors, including startups like Voltage Park and companies transitioning from other industries like Iris Energy, indicate the broadening appeal and utility of these GPUs. These diverse applications also contribute to the changing pricing dynamics as the market adjusts to varied demand.

Innovative financing models, where H100 GPUs are used as collateral, demonstrate the high value placed on these GPUs in the market. This trend not only signifies the intrinsic worth of these GPUs but also the evolving business models around GPU utilization.

The shortage of CoWoS packaging, essential for GPU manufacturing, has been a bottleneck in the supply chain. However, recent geopolitical developments, like U.S. restrictions on GPU shipments to Chinese companies, could unexpectedly ease these shortages and boost H100 production, potentially impacting the pricing and availability in various markets.

Cloud providers, including Oracle, Google, and Microsoft, expanding their H100 capacity through rental models and cloud services, are reshaping the market. These developments not only enhance access to advanced computing resources but also influence the pricing models in the cloud computing sector.

The narrative around the increasing capacity of Nvidia’s H100 GPUs and its impact on pricing is one of greater accessibility, diversified application, and evolving business models. As the supply of these GPUs stabilizes and broadens across various sectors and regions, the pricing dynamics are likely to become more competitive and favorable for a wider range of users.

Comparative Price Analysis: Nvidia H100 vs. Other GPUs


Nvidia H100 vs. Nvidia H200


  • The Nvidia H100 and H200 represent two of Nvidia’s flagship GPUs. The choice between these GPUs depends on specific requirements, such as data science applications or game development needs. While both GPUs offer high performance, the selection criteria are based on their distinct capabilities tailored for different professional applications.

Nvidia H100 vs. Nvidia A100


  • Price-wise, the Nvidia A100 series presents a varied range. For instance, the 80GB model of the A100 is priced at approximately $15,000, whereas the 40GB version can cost as much as $9,000. This pricing structure offers a comparison point against the H100’s market position.
  • In terms of performance, especially for scientific computing, the H100 and A100 are relatively matched. However, the A100 stands out for its high memory bandwidth and large cache, making it a strong choice for data-intensive tasks. Conversely, the H100 may have advantages in areas like gaming, attributed to its lower power consumption and faster clock speeds, potentially leading to more efficient and smoother gameplay.

Nvidia H100 vs. Other Competitors


  • The Nvidia H100 has been compared with other high-end GPUs like Biren’s BR104, Intel’s Sapphire Rapids, Qualcomm’s AI 100, and Sapeon’s X220. These comparisons are crucial for understanding the H100’s standing in the broader high-performance GPU market.
  • Technologically, the H100 marks a significant advancement, being manufactured on TSMC’s 4N process with 80 billion transistors. It offers up to 9 times faster speed than the A100, highlighting its superiority in certain applications. The H100 is also noted for being the first truly asynchronous GPU, which extends its capabilities beyond those of the A100.

This section of the article provides a detailed analysis of the Nvidia H100’s pricing and performance in comparison to other leading GPUs in the market. It serves to inform tech professionals and enthusiasts about the H100’s relative position and value proposition within the competitive landscape of high-performance GPUs.

Cost Justification for Buyers: Nvidia H100


AI Research and Machine Learning


  • Nvidia’s H100 GPU significantly elevates AI research and machine learning capabilities. Promising up to 9x faster AI training and up to 30x faster AI inference over its predecessor, the H100 becomes an essential tool for developing complex machine learning models, especially those utilizing Google’s Transformer Engine technology. This massive leap in performance justifies its cost for organizations aiming to lead in AI and ML innovations.

High-Performance Computing and Data Science


  • The H100’s superior computational prowess is a boon for high-performance computing (HPC) and data science. With more Tensor and CUDA cores, higher clock speeds, and an enhanced 80GB HBM3 memory, it offers unprecedented speed and capacity for complex computations in healthcare, robotics, quantum computing, and data science. This makes the H100 a valuable investment for sectors where cutting-edge computational ability is critical.

Cloud Service Providers and Large-Scale Computing


  • Cloud service providers stand to benefit significantly from the H100’s capabilities. The GPU’s advanced virtualization features allow it to be divided into seven isolated instances, enabling efficient resource allocation and enhanced data security through Confidential Computing. Furthermore, Nvidia’s DGX H100 servers and DGX POD/SuperPOD configurations, which utilize multiple H100 GPUs, offer scalable solutions for delivering AI services at an unprecedented scale. This scalability and efficiency make the H100 a strategic investment for cloud providers looking to offer competitive AI and machine learning services.

Server Manufacturers and Data Center Operators


  • For server manufacturers and data center operators, the H100 introduces new design paradigms, especially in terms of power and thermal management. While it has a higher performance per watt, its 700W TDP requires robust cooling solutions and power infrastructure. However, the ability to efficiently power and cool H100-equipped servers offers a competitive edge, as evidenced by the ‘DGX-Ready’ badge for data centers. This capacity for high performance in demanding environments justifies the H100’s cost for data centers looking to host the most advanced computing solutions.

This section elucidates the value proposition of the Nvidia H100 for different user groups, demonstrating why its cost is justified given its unparalleled capabilities in various computing domains.

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AI Research and Innovations with Nvidia H100

AI Research and Innovations with Nvidia H100

AI Research and Innovations with Nvidia H100

Nvidia H100

Introduction to Nvidia H100 and Its Role in AI Research


The Nvidia H100, a marvel in modern computing, represents a significant leap in the world of artificial intelligence (AI) and high-performance computing (HPC). As the latest innovation from Nvidia, the H100 chip is not just an upgrade; it’s a complete transformation in the way AI research and development is conducted.

The Evolution of GPU Computing


The H100 stands as a testament to the rapid evolution of GPU computing. Marking a distinct departure from its predecessor, the A100, the H100 showcases up to nine times faster AI training capabilities and a staggering thirty times increase in inference speed. This unparalleled performance is rooted in its architectural design, which houses an astounding 80 billion transistors. These transistors are not just a numerical feat; they are the engines driving the H100’s capacity to handle complex AI modeling and research tasks with unprecedented efficiency and speed.

Pioneering High-Performance Computing


A look at the world’s fastest supercomputers reveals the transformative impact of the H100. The latest TOP500 list, which ranks the globe’s most powerful supercomputers, has observed a notable shift towards accelerated and energy-efficient computing, primarily driven by systems powered by the H100. With these advancements, Nvidia has achieved more than 2.5 exaflops of HPC performance across leading systems, a significant increase from the previous 1.6 exaflops. This leap in computational capability is not just a numerical achievement; it’s a cornerstone in the advancement of scientific research, enabling researchers to tackle previously insurmountable challenges in various fields.

Accelerating AI Development and Deployment


The H100’s impact extends beyond raw computing power. It significantly accelerates the development and deployment of AI applications. Combined with the NVIDIA AI Enterprise software suite, the H100 allows organizations to develop AI solutions at an unprecedented pace, enhancing performance and reducing time-to-market. This acceleration is crucial in the rapidly evolving AI landscape, providing organizations a competitive edge through faster innovation and implementation of AI technologies.

Optimizing for Generative AI and Large Language Models


At the core of the H100’s design is the NVIDIA Hopper GPU computing architecture, featuring a built-in Transformer Engine. This architecture is specifically optimized for developing, training, and deploying generative AI, large language models (LLMs), and recommender systems. The H100 leverages FP8 precision, offering a ninefold increase in AI training speed and up to thirty times faster AI processing. This level of optimization is pivotal for the current and future landscape of AI, where large language models and generative AI are becoming increasingly central.

In conclusion, the Nvidia H100 is more than just a GPU; it’s a harbinger of a new era in AI research and innovation. Its capabilities in accelerating AI training, enhancing inference speed, and pushing the boundaries of HPC, position it as a key driver in the ongoing AI revolution.

Performance Leap: Analyzing the H100’s Capabilities


The Nvidia H100 Tensor Core GPU is a groundbreaking advancement in the realm of artificial intelligence (AI) and high-performance computing (HPC), embodying a quantum leap in performance, scalability, and efficiency. This section delves into the technical prowess of the H100, elucidating its capabilities that redefine the boundaries of computational science.

Architectural Mastery: The H100’s Core Specifications


The Nvidia H100, based on the cutting-edge NVIDIA Hopper architecture, showcases a revolutionary leap in GPU design and functionality. Equipped with 80 GB HBM2e memory, the H100 PCIe 80 GB variant demonstrates the synergy of massive memory capacity and high-speed processing. Operating at a base frequency of 1095 MHz, which can be boosted up to 1755 MHz, the H100 PCIe 80 GB exemplifies the blend of power and precision. Its 5120-bit memory interface further enhances its capability to handle extensive data sets with remarkable efficiency.

Powering Exascale Workloads: Scalability and Connectivity


The H100’s prowess extends to its scalability. With the NVIDIA® NVLink® Switch System, up to 256 H100 GPUs can be interconnected, creating a powerhouse for accelerating exascale workloads. This capability is not just about connecting multiple GPUs; it’s about creating a cohesive and powerful network that can tackle the most demanding computational challenges with ease. This interconnected ecosystem facilitates the processing of enormous data sets, crucial for advancements in areas like climate modeling, astrophysics, and complex systems simulation.

A New Dimension in AI Processing: The Transformer Engine


At the heart of the H100’s design is its dedicated Transformer Engine. This innovative feature is tailored to support trillion-parameter language models, placing the H100 at the forefront of AI research, particularly in the development of large language models (LLMs) and generative AI. This specialized engine is a game-changer, providing the computational muscle needed to train and deploy some of the most complex AI models in existence today. By offering such targeted support for AI tasks, the H100 sets a new benchmark in the field of AI research and development.

A Fusion of Speed and Efficiency


The H100 PCIe 80 GB version, another variant of this GPU series, is built on a 4 nm process and is centered around the GH100 graphics processor. This configuration highlights Nvidia’s commitment to optimizing both performance and energy efficiency. The absence of support for DirectX 11 or DirectX 12 in this variant underscores its specialized focus on professional, high-performance computing applications rather than traditional gaming.

In summary, the Nvidia H100 Tensor Core GPU stands as a testament to Nvidia’s innovative spirit, pushing the frontiers of what’s possible in AI and HPC. Its exceptional capabilities in memory capacity, processing speed, scalability, and specialized AI support make it a cornerstone technology for the next generation of AI research and innovations.

Scaling AI with Nvidia H100: Case Studies and Applications


The NVIDIA H100 GPU has ushered in a new era of computing, revolutionizing various industries and scientific research fields. This section highlights significant case studies and applications of the H100, showcasing its transformative impact.

Empowering Supercomputing and AI Performance

The integration of NVIDIA H100 GPUs into supercomputing systems has dramatically enhanced their capabilities. NVIDIA now delivers over 2.5 exaflops of HPC performance across world-leading systems, a substantial increase from the previous 1.6 exaflops. This enhancement is vividly seen in the latest TOP500 list, where NVIDIA’s contribution includes 38 of 49 new supercomputers. Such systems, like Microsoft Azure’s Eagle system and the Mare Nostrum5 in Barcelona, leverage H100 GPUs to achieve groundbreaking performance, demonstrating both power and energy efficiency.

Advanced Research in Biomolecular Structures


A notable application of the H100 GPU is at Argonne National Laboratory, where NVIDIA’s BioNeMo, a generative AI platform, was used to develop GenSLMs. This model can generate gene sequences closely resembling real-world variants of the coronavirus. Leveraging the power of NVIDIA GPUs and a vast dataset of COVID genome sequences, it has the capability to rapidly identify new virus variants. This groundbreaking work, which won the Gordon Bell special prize, highlights the H100’s potential in advancing medical and biological research.

Accelerating Automotive Engineering


In the automotive industry, the H100 GPU has made a significant impact. Siemens, in collaboration with Mercedes, utilized the H100 GPUs to analyze the aerodynamics and acoustics of its new electric EQE vehicles. What previously took weeks on CPU clusters was significantly accelerated using the H100, demonstrating its efficiency and power in handling complex simulations. This case study is a testament to how the H100 GPU can transform industry standards, reducing both computational time and energy consumption.

In summary, the NVIDIA H100 GPU is not just a technological advancement; it’s a catalyst for innovation across diverse fields. From supercomputing and healthcare to automotive engineering, the H100 is reshaping the landscape of AI research and application, offering unprecedented performance and efficiency.

The H100’s Impact on Cloud Computing and GPU Servers


The integration of Nvidia H100 GPUs into cloud computing and GPU servers represents a paradigm shift in computational capabilities and resource accessibility. This section explores how the H100 has been integrated into cloud environments and its implications for GPU server solutions.

Architectural Innovations and Performance Boost

The H100 GPU, with its architectural innovations, including fourth-generation Tensor Cores and a new Transformer Engine, is optimized for accelerating large language models (LLMs). This technology is pivotal in enhancing the capabilities of cloud computing environments, allowing for supercomputing-class performance. The latest NVLink technology, facilitating communication between GPUs at an incredible speed of 900GB/sec, further enhances this performance, enabling the handling of more complex and demanding computational tasks.


Streamlining AI Application Development

NVIDIA AI Enterprise, designed to streamline the development and deployment of AI applications, addresses the complexities of building and maintaining a high-performance, secure, cloud-native AI software platform. Available in the AWS Marketplace, it offers continuous security monitoring, API stability, and access to NVIDIA AI experts, enhancing the overall efficiency and security of AI application development in the cloud.

In conclusion, the integration of the NVIDIA H100 GPUs into cloud computing and GPU server solutions like AWS’s EC2 P5 instances significantly enhances the capabilities and accessibility of high-performance computing resources. This integration represents a major leap in the evolution of cloud computing, offering unprecedented power and flexibility for a wide range of AI and HPC applications.

Advancements in AI Models: Large Language Models and Beyond

The NVIDIA H100 Tensor Core GPU is at the forefront of advancements in AI models, especially in the deployment of large language models (LLMs) and generative AI. This section delves into how the H100’s capabilities are revolutionizing these domains.

Launch of Specialized Inference Platforms


NVIDIA recently launched four inference platforms, significantly optimized for a wide range of emerging generative AI applications. These platforms, combining NVIDIA’s comprehensive suite of inference software, feature the NVIDIA H100 NVL GPU and other advanced processors. Each platform is meticulously optimized for specific workloads, such as AI video, image generation, LLM deployment, and recommender inference. This strategic development empowers developers to build specialized, AI-powered applications swiftly, delivering new services and insights with enhanced efficiency.

The H100 NVL: A Game-Changer for Large Language Models


The H100 NVL variant of the GPU stands out as an ideal choice for deploying massive LLMs like ChatGPT at scale. Boasting 94GB of memory and the transformative Transformer Engine acceleration, the H100 NVL delivers up to 12 times faster inference performance at GPT-3 compared to the previous generation A100 at data center scale. This significant performance boost is pivotal for large-scale deployments of LLMs, enabling more efficient and effective processing of complex language tasks and AI-driven interactions.

Enhancing AI Development with NVIDIA AI Enterprise Software Suite


To complement the hardware advancements, the platforms’ software layer includes the NVIDIA AI Enterprise software suite. This suite features NVIDIA TensorRT, a software development kit for high-performance deep learning inference, and NVIDIA Triton Inference Server, an open-source inference-serving software that standardizes model deployment. These software tools are essential for developers, allowing them to harness the full potential of the H100 for diverse AI applications. The combined hardware and software solutions offer an integrated approach to advancing AI models, ensuring high performance, scalability, and ease of deployment.

In conclusion, the NVIDIA H100, particularly the H100 NVL GPU, marks a significant advancement in the field of AI. Its ability to efficiently deploy and process large language models and its integration with NVIDIA’s comprehensive software suite redefine the possibilities in AI research and application, setting a new benchmark for future innovations.

Enhanced Security and Scalability with H100


The NVIDIA H100 GPU introduces groundbreaking advancements in security and scalability, addressing critical aspects of modern computing, particularly in AI and HPC environments. This section focuses on how the H100 enhances the security and scalability of computing workloads.

Revolutionizing Data Protection: Confidential Computing


The H100 is the first GPU to support confidential computing, a transformative approach to secure data processing. By isolating workloads in virtual machines (VMs) from each other and the physical hardware, the H100 offers improved security in multi-tenant environments. This technology is particularly vital when dealing with sensitive data like personally identifiable information (PII) or enterprise secrets during AI training or inference, ensuring confidentiality, integrity, and availability.

Trusted Execution Environment (TEE) for AI Security


The H100’s Trusted Execution Environment (TEE) is anchored in an on-die hardware root of trust (RoT), establishing a secure computing foundation. When the H100 boots in Confidential Computing (CC-On) mode, it enables hardware protections for code and data, thus establishing a chain of trust. This environment ensures that AI models and data are processed in a hardware-based, attested TEE, providing robust protection against various security threats.

Confidential Computing Modes and Scalability


The H100 supports multiple confidential computing modes, including CC-Off (standard operation), CC-On (full activation of confidential computing features), and CC-DevTools (partial CC mode with security protections disabled for development purposes). These modes enhance the H100’s versatility in different use cases, from development to deployment, ensuring both security and performance. Additionally, the H100 works with CPUs supporting confidential VMs (CVMs) to extend TEE protections to the GPU, allowing encrypted data transfers between the CPU and GPU.

Hardware-based Security and Isolation


To ensure full isolation of VMs, the H100 encrypts data transfers between the CPU and GPU, creating a physically isolated TEE with built-in hardware firewalls. This secure environment protects the entire workload on the GPU, offering an added layer of security in diverse computing environments, from on-premises to cloud and edge deployments.

Simplified Deployment with No Code Changes


The H100 allows organizations to leverage the benefits of confidential computing without requiring changes to their existing GPU-accelerated workloads. This feature ensures that applications can maintain security, privacy, and regulatory compliance while leveraging the H100’s enhanced capabilities.

Accelerated Computing Performance in Confidential Mode


The H100’s confidential computing architecture is compatible with CPU architectures that support application portability between non-confidential and confidential computing environments. This compatibility ensures that the performance of confidential computing workloads on the GPU remains close to that of non-confidential computing mode, especially when the compute demand is high compared to the amount of input data.

In summary, the NVIDIA H100 GPU’s enhanced security features, including confidential computing, hardware-based TEE, and scalable operational modes, provide a robust and flexible solution for secure and efficient AI and HPC workloads. These advancements position the H100 as a key technology in the secure and scalable processing of sensitive data and complex computational tasks.


The introduction of the NVIDIA H100 GPU is set to significantly influence future trends and predictions in AI research and innovation. This section explores the emerging trends and potential impact of the H100 on AI development.

Accelerating Generative AI and Large Language Models


The NVIDIA H100, with its advanced Hopper architecture and Transformer Engine, is optimized for developing, training, and deploying generative AI and large language models (LLMs). Its FP8 precision significantly accelerates AI training and inference, offering up to 9 times faster AI training and 30 times faster AI inference on LLMs compared to the A100. This leap in performance is essential for driving the next wave of AI, particularly in generative AI and LLM applications, where speed and efficiency are critical.

Enhancing Enterprise AI with DGX H100


The NVIDIA DGX H100 system, featuring eight H100 GPUs connected with NVIDIA NVLink high-speed interconnects, provides a potent platform for enterprise AI. Offering 32 petaflops of compute performance at FP8 precision and integrated networking capabilities, the DGX H100 maximizes energy efficiency in processing large AI workloads. It also includes the complete NVIDIA AI software stack, simplifying AI development and operations at scale. This comprehensive solution is poised to revolutionize enterprise AI, enabling seamless management of extensive AI workloads.

Expanding the Reach of AI Applications


Organizations like OpenAI, Stability AI, Twelve Labs, and Anlatan are leveraging the H100 to enhance their AI research and applications. For example, OpenAI plans to use the H100 in its Azure supercomputer for ongoing AI research, including the development of advanced dialogue systems. Stability AI intends to use the H100 to accelerate video, 3D, and multimodal models, while Twelve Labs aims to use the H100 for multimodal video understanding. Anlatan is utilizing the H100 for AI-assisted story writing and text-to-image synthesis. These diverse applications highlight the H100’s versatility in driving a wide range of AI innovations.

The Future Landscape of AI Research


The NVIDIA H100 is positioned to be a cornerstone in the future landscape of AI research, with its unparalleled capabilities in processing speed, efficiency, and scalability. Its influence will likely extend to various domains, from healthcare and automotive to entertainment and finance, driving innovations that were previously unattainable. As AI continues to evolve, the H100 will play a pivotal role in shaping how AI models are developed and deployed, heralding a new era of AI-driven solutions and services.

In conclusion, the NVIDIA H100 GPU is not just a technological advancement; it is a catalyst for a new era in AI research and application. With its exceptional capabilities, it sets the stage for transformative AI innovations and paves the way for future breakthroughs in various fields.

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Conclusion: Nvidia H100’s Pivotal Role in Shaping AI’s Future


The Nvidia H100’s advent marks a revolutionary stride in the sphere of artificial intelligence (AI) and high-performance computing (HPC), reshaping the technological landscape. This concluding section reflects on the comprehensive impact of the H100, underlining its transformative role in AI and HPC.

Foundational Role in AI and Deep Learning


GPUs, epitomized by advancements like the H100, have become fundamental to AI. Their ability to efficiently handle large neural networks has revolutionized fields like deep learning, enabling breakthroughs in autonomous driving and facial recognition. The H100, with its superior processing capabilities, pushes these boundaries further, ensuring AI remains at the forefront of technological innovation.


The H100’s integration into cloud computing and HPC signifies one of the hottest trends in enterprise technology. By enabling tasks traditionally reserved for supercomputers, the H100 democratizes access to immense computational power, saving time and resources. This integration enhances cloud computing’s capacity, making it a more viable and efficient option for handling extensive computational workloads.

Revolutionizing Parallel Processing and Computational Efficiency


Since its inception, the GPU’s role has evolved from handling graphics-intensive tasks to dominating parallel processing in AI and HPC. The H100, with its advanced capabilities, exemplifies this evolution. It dramatically outperforms CPUs in processing efficiency, particularly in scenarios requiring parallel computation, making previously impossible tasks feasible. This efficiency is pivotal for processing high-resolution images, complex AI algorithms, and large data sets, marking a new era in computational power.

Facilitating Development and Deployment of AI Applications


NVIDIA’s development of platforms like CUDA and partnerships with entities like Red Hat OpenShift has significantly streamlined the development and deployment of AI applications. This collaboration has simplified the integration of GPUs with Kubernetes, making the process more efficient and less prone to errors. The H100 benefits from these advancements, offering an optimized environment for developing and deploying AI applications with enhanced ease and efficiency.

In summary, the NVIDIA H100 GPU’s impact extends beyond its technical prowess. It is a beacon of innovation in AI and HPC, driving advancements across various sectors and paving the way for future breakthroughs. The H100’s introduction is not just an upgrade in GPU technology; it is a harbinger of a new chapter in the AI and computing revolution.

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High-Performance Computing with the Nvidia H100

High-Performance Computing with the Nvidia H100

High-Performance Computing with the Nvidia H100

Nvidia H100


In the realm of computational technology, the evolution of high-performance computing (HPC) has been nothing short of revolutionary. At the core of this transformation lies the Graphics Processing Unit (GPU), a key player in the acceleration of complex computations across various domains. Historically, the term GPU was popularized in 1999 by Nvidia with its GeForce 256, renowned for its proficiency in graphics transformation and 3D rendering capabilities. This was a pivotal moment, as it marked the transition of GPUs from solely graphical tasks to broader computational realms.

GPUs have since become the bedrock of artificial intelligence and machine learning, offering unparalleled processing power essential for today’s demanding applications. Before the widespread use of GPUs, machine learning processes were slower, less accurate, and often inadequate for complex tasks. GPUs transformed this landscape, enabling rapid processing of large neural networks and deep learning algorithms, which are now integral to technologies like autonomous driving and facial recognition.

The transition of GPUs into HPC is one of the most significant trends in enterprise technology today. Cloud computing, combined with the power of GPUs, creates a seamless and efficient process for tasks traditionally reserved for supercomputers. This synergy not only saves time but also significantly reduces costs, making it an invaluable asset in various sectors.

Moreover, GPUs have demonstrated their ability to outperform multiple CPUs, especially in tasks requiring parallel processing. For example, processing high-resolution images or videos, which would take years on a single CPU, can be accomplished in a day with a few GPUs. This capability of handling massive volumes of data at incredible speeds has opened doors to previously impossible tasks.

In 2006, Nvidia further pushed the boundaries with the introduction of CUDA, a parallel computing platform that allows developers to utilize GPUs more efficiently. CUDA enables the partitioning of complex problems into smaller, independent tasks, harnessing the full potential of Nvidia’s GPU architecture for a wide range of applications, from scientific research to real-time data processing.

As we delve deeper into the specifics of the Nvidia H100 and its impact on high-performance computing, it’s crucial to recognize this backdrop of GPU evolution. The H100 represents the latest in a lineage of technological advancements, standing as a testament to the relentless pursuit of computational excellence and efficiency.

Overview of Nvidia H100 GPU

The NVIDIA H100 Tensor Core GPU, representing the ninth generation in NVIDIA’s lineup, is a groundbreaking advancement in data center GPU technology. Designed to significantly elevate performance for AI and HPC applications, the H100 leapfrogs its predecessor, the A100, in terms of efficiency and architectural design. This transformative step is particularly evident in mainstream AI and HPC models, where the H100, equipped with InfiniBand interconnect, achieves up to 30 times the performance of the A100. The introduction of the NVLink Switch System further amplifies its capabilities, targeting complex computing workloads requiring model parallelism across multiple GPU nodes.

In 2022, NVIDIA announced the NVIDIA Grace Hopper Superchip, incorporating the H100 GPU. This product is engineered for terabyte-scale accelerated computing, offering a performance boost up to tenfold for large-model AI and HPC. The H100 pairs with the NVIDIA Grace CPU, utilizing an ultra-fast chip-to-chip interconnect and delivering an impressive 900 GB/s of total bandwidth, significantly outperforming current server standards.

The H100’s architecture includes several innovative features:

  • Fourth-Generation Tensor Cores: These cores are up to six times faster than those in the A100, supporting advanced data types like FP8, which enhances deep learning network performance.
  • Enhanced Processing Rates: The H100 boasts a threefold increase in IEEE FP64 and FP32 processing rates compared to the A100, owing to faster performance per streaming multiprocessor (SM) and increased SM counts.
  • Thread Block Cluster Feature: This new feature extends the CUDA programming model, allowing for synchronized data exchange across multiple SMs, optimizing parallel processing.
  • Distributed Shared Memory and Asynchronous Execution: These features enable efficient SM-to-SM communication and data transfer, bolstering the GPU’s processing capability.
  • Transformer Engine: A combination of software and custom Hopper Tensor Core technology, this engine significantly accelerates transformer model training and inference, offering up to 9x faster AI training and 30x faster AI inference on large language models compared to the A100.
  • HBM3 Memory Subsystem: The H100 introduces the world’s first GPU with HBM3 memory, delivering a remarkable 3 TB/sec of memory bandwidth.
  • L2 Cache Architecture: A 50 MB L2 cache reduces the frequency of memory accesses, enhancing data processing efficiency.
  • Multi-Instance GPU (MIG) Technology: The second-generation MIG technology in the H100 provides substantially more compute capacity and memory bandwidth per instance, alongside introducing Confidential Computing at the MIG level.
  • Confidential Computing Support: This new feature provides enhanced data security and isolation in virtualized environments, establishing the H100 as the first native Confidential Computing GPU.
  • Fourth-Generation NVLink: This technology improves bandwidth for multi-GPU operations, offering a 50% general increase and 3x bandwidth enhancement for specific operations over the previous generation.

These architectural enhancements make the NVIDIA H100 a formidable tool in the high-performance computing landscape, setting new standards in processing power, efficiency, and security.

Advancements in AI and Machine Learning

The arrival of the NVIDIA H100 Tensor Core GPU ushers in a new era for artificial intelligence (AI) and machine learning (ML), marking a significant advancement from the previous-generation A100 SXM GPU. The H100 stands out with its capability to deliver three times the throughput on Tensor Core, including FP32 and FP64 data types, a result of the next-generation Tensor Cores, an increased number of streaming multiprocessors (SMs), and a higher clock frequency.

A key innovation in the H100 is its support for the FP8 data type and the integration of the new Transformer Engine, which together enable six times the throughput compared to the A100. The Transformer Engine, in particular, accelerates AI calculations for transformer-based models, such as large language models, which are pivotal in today’s AI advancements.

The H100’s performance in real-world deep learning applications varies by workload. For instance, language models generally see a greater benefit, with approximately four times the speedup compared to vision-based models, which experience around a two times speedup. Significantly, certain large language models that necessitate model parallelization can achieve up to a 30-fold increase in inference speed. This enhancement is particularly valuable for applications involving structured sparsity, such as in natural language processing, vision, and drug design, and for large-scale distributed workloads.

In terms of architecture, the H100 introduces several key improvements over its predecessor:

  • Fourth-Generation Tensor Cores: Each SM of the H100 doubles the computational throughput of the A100 SM, enhancing the efficiency of fundamental deep learning building blocks like General Matrix Multiplications (GEMMs).
  • Increased SMs and Clock Frequencies: With more SMs and higher operating frequencies, the H100 offers a substantial improvement in computational capacity.
  • FP8 and Transformer Engine: The addition of the FP8 data type and the Transformer Engine not only quadruples computational rates but also intelligently manages calculations, optimizing both memory usage and performance while maintaining accuracy.

These advancements contribute to a significant overall upgrade for all deep learning applications, optimizing the H100 for the largest and most complex models. The H100’s ability to handle tasks involving large language, vision, or life sciences models marks it as a pivotal tool in the evolution of AI and ML, driving forward the boundaries of what is computationally possible.

High-Performance Computing (HPC) Applications

The NVIDIA H100 has emerged as a pivotal tool in High-Performance Computing (HPC), offering transformative advancements in various scientific and data analytic applications. The H100’s capacity to handle compute-intensive tasks effectively bridges the gap between traditional HPC workloads and the increasingly intertwined fields of AI and ML.

Enhanced Performance in Scientific Computing

The H100 has significantly impacted generative AI, particularly in training and deploying large language models (LLMs) like OpenAI’s GPT models and Meta’s Llama 2, as well as diffusion models like Stability.ai’s Stable Diffusion. These models, with their massive parameter sizes and extensive training data, demand a level of computational performance that transcends the capabilities of single GPUs or even single-node GPU clusters.

Reflecting the convergence of HPC and AI, the MLPerf HPC v3.0 benchmarks now include tests like protein structure prediction using OpenFold, atmospheric river identification for climate studies, cosmology parameter prediction, and quantum molecular modeling. These benchmarks highlight the H100’s ability to efficiently train AI models for complex scientific computing applications.

In recent MLPerf Training rounds, NVIDIA demonstrated the H100’s unprecedented performance and scalability for LLM training. It significantly improved the per-accelerator performance of the H100 GPU, leading to faster training times and reduced costs. This improvement extends to a variety of workloads, including text-to-image training, DLRM-dcnv2, BERT-large, RetinaNet, and 3D U-Net, setting new performance records at scale.

Applications in Data Analytics

In the realm of data analytics, particularly in financial risk management, the H100 has set new standards in efficiency and performance. During a recent STAC-A2 audit, a benchmark for compute-intensive analytic workloads in finance, H100-based solutions set several performance records. These achievements include faster processing times and better energy efficiency for tasks like Monte Carlo estimation and risk analysis.

Financial High-Performance Computing (HPC) has greatly benefited from the H100’s capabilities. The H100 enables efficient node strategies for intensive calculations like price discovery, market risk, and counterparty risk, significantly reducing the number of nodes required for such tasks. This reduction in nodes translates to higher performance and lower operational costs, highlighting the H100’s role in scaling up financial HPC with fewer resources.

Moreover, NVIDIA provides a comprehensive software layer for the H100, offering developers various tools and programming languages like CUDA C++ for optimized calculations. This approach, coupled with the H100’s performance, enables faster runtime for critical applications, underscoring its versatility and power in data analytics.

The H100 stands as a cornerstone in the NVIDIA data center platform, built to accelerate over 4,000 applications in AI, HPC, and data analytics. Its groundbreaking technology includes a peak performance of 51 TFLOPS for single precision and 26 TFLOPS for double-precision calculations, bolstered by 14,592 CUDA cores and 456 fourth-generation Tensor Core modules. With these features, the H100 not only triples the theoretical floating-point operations per second (FLOPS) of FP64 compared to the A100 but also enhances performance with dynamic programming instructions (DPX).

In summary, the NVIDIA H100 is revolutionizing the HPC landscape, providing unparalleled performance and efficiency across a wide array of scientific and data analytic applications. Its impact extends from complex scientific computations to intensive data analytics in finance, marking a new era in high-performance computing capabilities.

Security and Confidential Computing

The NVIDIA H100 Tensor Core GPU represents a significant advancement in the realm of secure and confidential computing. Confidential computing, a critical aspect of modern computing infrastructure, ensures the protection of data in use, addressing the vulnerabilities that persist in traditional data protection methods. The H100 stands as the first-ever GPU to introduce support for confidential computing, a leap forward in data security for AI and HPC environments.

NVIDIA Confidential Computing Using Hardware Virtualization

Confidential computing on the H100 is achieved through hardware-based, attested Trusted Execution Environments (TEE). The H100’s TEE is grounded in an on-die hardware root of trust (RoT). When operating in CC-On mode, the GPU activates hardware protections for both code and data, establishing a secure environment through:

  • Secure and measured GPU boot sequence.
  • Security protocols and data models (SPDM) session for secure driver connections.
  • Generation of a cryptographically signed attestation report, ensuring the integrity of the computing environment.

Evolution of GPU Security

NVIDIA has continually enhanced the security features of its GPUs. Starting with AES authentication in the Volta V100 GPU, subsequent architectures like Turing and Ampere introduced encrypted firmware and fault injection countermeasures. The Hopper architecture, embedded within the H100, adds on-die RoT and measured/attested boot, reinforcing the security framework necessary for confidential computing. This comprehensive approach, encompassing hardware, firmware, and software, ensures the protection and integrity of both code and data in the H100 GPU.

Hardware Security for NVIDIA H100 GPUs

The H100 GPU’s confidential computing capabilities extend across multiple products, including the H100 PCIe, H100 NVL, and NVIDIA HGX H100. It supports three operational modes:

  • CC-Off: Standard operation without confidential computing features.
  • CC-On: Full activation of confidential computing features, including active firewalls and disabled performance counters to prevent side-channel attacks.
  • CC-DevTools: Partial confidential computing mode with performance counters enabled for development purposes.

Operating NVIDIA H100 GPUs in Confidential Computing Mode

In confidential computing mode, the H100 GPU works with CPUs supporting confidential VMs (CVMs), ensuring that operators cannot access the contents of CVM or confidential container memory. The NVIDIA driver facilitates secure data movement to and from GPU memory through encrypted bounce buffers and signed command buffers and CUDA kernels. This system ensures that running CUDA applications on the H100 in confidential computing mode remains seamless and secure.

In summary, the NVIDIA H100 GPU’s introduction of confidential computing capabilities marks a transformative step in securing data during computation, catering to the increasing demand for robust security solutions in AI and HPC applications.

Case Studies and Real-world Applications

The NVIDIA H100 GPU is powering a wide range of industries, showcasing its versatility and robustness in various real-world applications. From enhancing research capabilities in educational institutions to revolutionizing cloud computing and driving innovation in multiple sectors, the H100 is proving to be a transformative technology.

Academic and Research Institutions

Leading universities and research institutions are harnessing the power of the H100 to advance their computational capabilities. Institutions like the Barcelona Supercomputing Center, Los Alamos National Lab, Swiss National Supercomputing Centre (CSCS), Texas Advanced Computing Center, and the University of Tsukuba are utilizing the H100 in their next-generation supercomputers. This integration of advanced GPUs into academic research is enabling these institutions to push the boundaries in fields such as climate modeling, astrophysics, and life sciences.

Cloud Computing and Service Providers

Major cloud service providers like Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure are among the first to deploy H100-based instances. This deployment is set to accelerate the development and application of AI worldwide, particularly in areas requiring intense computational power, such as healthcare, autonomous vehicles, robotics, and IoT applications.

Manufacturing and Industrial Applications

In the manufacturing sector, applications like Green Physics AI are leveraging the H100 to predict the aging of factory equipment, aiming to make future plants more efficient. This tool provides insights into an object’s CO2 footprint, age, and energy consumption, enabling the creation of powerful AI models and digital twins that optimize factory and warehouse efficiency.

Robotics and AI Research

Organizations like the Boston Dynamics AI Institute are using the H100 for groundbreaking research in robotics. The focus is on developing dexterous mobile robots that can assist in various settings, including factories, warehouses, and even homes. This initiative requires advanced AI and robotics capabilities, which the H100 is uniquely positioned to provide.

Startups are also harnessing the H100’s capabilities. Scissero, a legal tech company, employs a GPT-powered chatbot for drafting legal documents and conducting legal research. In language services, DeepL uses the H100 to enhance its translation services, offering AI-driven language solutions to clients worldwide.

Healthcare Advancements

In healthcare, the H100 is facilitating advancements in drug discovery and patient care. In Tokyo, the H100 is part of the Tokyo-1 supercomputer, accelerating simulations and AI for drug discovery. Hospitals and academic healthcare organizations globally are also among the first users of the H100, demonstrating its potential in improving healthcare outcomes.

Diverse University Research

Universities globally are integrating H100 systems for a variety of research projects. For instance, Johns Hopkins University’s Applied Physics Laboratory is training large language models, while the KTH Royal Institute of Technology in Sweden is enhancing its computer science programs. These use cases highlight the H100’s role in advancing educational and research endeavors across disciplines.

In conclusion, the NVIDIA H100 GPU is at the forefront of computational innovation, driving progress and efficiency across diverse sectors. Its impact is evident in the multitude of industries and disciplines it is transforming, solidifying its role as a cornerstone technology in the modern computational landscape.

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Deep Learning Training and Inference on Nvidia H100

Deep Learning Training and Inference on Nvidia H100

Deep Learning Training and Inference on Nvidia H100

RTX A5000

Understanding the Nvidia H100 GPU


NVIDIA H100 Tensor Core GPU: A Leap in Data Center GPU Technology


The NVIDIA H100 Tensor Core GPU marks a significant milestone as the ninth-generation data center GPU from NVIDIA. It’s designed to provide a substantial performance leap over its predecessor, the NVIDIA A100 Tensor Core GPU, particularly for large-scale AI and High-Performance Computing (HPC) applications. This GPU maintains a focus on improving strong scaling for AI and HPC workloads, complemented by significant architectural enhancements.

Key Features of the H100 GPU


  • Streaming Multiprocessor (SM) Innovations: The H100 introduces a new streaming multiprocessor design with numerous performance and efficiency improvements. It features the fourth-generation Tensor Cores, which offer up to six times faster performance compared to the A100. These Cores provide double the Matrix Multiply-Accumulate (MMA) computational rates on equivalent data types and quadruple the rate using the new FP8 data type. Additionally, the Sparsity feature in these Cores effectively doubles the performance of standard Tensor Core operations.
  • Enhanced Dynamic Programming (DPX) Instructions: The H100 GPU introduces new DPX instructions that accelerate dynamic programming algorithms, achieving up to seven times faster performance than the A100 GPU. Examples include the Smith-Waterman algorithm for genomics processing and the Floyd-Warshall algorithm for optimal routing in dynamic environments.
  • Advanced IEEE FP64 and FP32 Processing Rates: The H100 achieves three times faster processing rates compared to A100, a result of faster per SM performance, additional SM counts, and higher clock speeds.
  • Thread Block Cluster and Distributed Shared Memory: The H100 features a new thread block cluster that allows for programmatic control of locality on a larger scale than a single thread block on a single SM. This addition to the CUDA programming model enhances data synchronization and exchange across multiple SMs. The distributed shared memory further enables direct SM-to-SM communications.
  • Asynchronous Execution Capabilities: The H100 integrates new asynchronous execution features, including the Tensor Memory Accelerator (TMA) for efficient data transfer between global and shared memory and supports asynchronous copies within a cluster.

The NVIDIA Hopper GPU Architecture


The H100 GPU is based on the cutting-edge NVIDIA Hopper architecture, which brings multiple innovations to the table:

  • New Fourth-Generation Tensor Cores: These Cores are designed for faster matrix computations, crucial for a broad range of AI and HPC tasks.
  • Transformer Engine: This new addition enables the H100 to deliver significantly faster AI training and inference speedups, especially on large language models, compared to the A100.
  • NVLink Network Interconnect: This feature allows efficient GPU-to-GPU communication among up to 256 GPUs across multiple compute nodes, facilitating large-scale distributed workloads.
  • Secure MIG Technology: It partitions the GPU into isolated instances, optimizing quality of service for smaller workloads.

Technical Specifications of the H100 GPU


The heart of the H100 GPU, the GH100, is built using the TSMC 4N process tailored for NVIDIA, featuring 80 billion transistors and a die size of 814 mm². The H100 GPU with SXM5 board form-factor comprises 8 GPU Processing Clusters (GPCs), 66 Texture Processing Clusters (TPCs), and 132 Streaming Multiprocessors (SMs). It includes 16896 FP32 CUDA Cores and 528 fourth-generation Tensor Cores. The GPU is equipped with 80 GB of HBM3 memory, providing a staggering 3 TB/sec of memory bandwidth, and includes a 50 MB L2 cache.


Deep Learning Training Performance


The Significance of AI Model Training


Training AI models is a cornerstone of the rapidly growing AI application landscape. The efficiency of this training process is crucial, as it impacts the deployment speed and the overall value generation of AI-powered applications. The NVIDIA H100, with its advanced capabilities, plays a pivotal role in enhancing this training efficiency, enabling more rapid development and deployment of AI models.

MLPerf Training v3.0: Setting New Benchmarks


MLPerf Training v3.0, a suite of tests developed by MLCommons, measures AI performance across various use cases. The inclusion of new tests, like the large language model (LLM) based on GPT-3 and an updated DLRM test, provide a more comprehensive evaluation of AI training performance. The NVIDIA H100 set new performance records in MLPerf Training v3.0, achieving the highest performance on a per-accelerator basis and delivering the fastest time to train on every benchmark at scale. This demonstrates the H100’s capability to handle a wide range of AI training tasks, from computer vision to language processing and recommender systems.

Record-Setting Performance in Diverse Workloads


NVIDIA H100 GPUs achieved unprecedented performance in MLPerf Training v3.0, setting new time-to-train records across various workloads. This includes large-scale tasks such as training the state-of-the-art LLM with 175 billion parameters and other demanding applications in natural language processing, image classification, and more. The H100 GPUs significantly reduced the time-to-train across these diverse tasks, showcasing their ability to handle the most challenging AI training workloads efficiently.

Training Large Language Models: A Case Study


The training of large language models, like the GPT-3 with 175 billion parameters, requires a robust full-stack approach, stressing every aspect of an AI supercomputer. The NVIDIA H100 GPUs demonstrated their capability in this demanding environment by achieving significant time-to-train reductions, even when scaled to hundreds or thousands of GPUs. This shows the H100’s ability to maintain high performance in both on-premises and cloud-based AI training environments.

Optimizations in AI Model Training


NVIDIA’s submissions for MLPerf Training v3.0 included various optimizations that enhanced the H100’s performance in training AI models. For instance, improvements in data preprocessing and random number generation led to significant reductions in iteration time and increased throughput. Additionally, the use of CUDA Graphs and optimizations in the cuBLAS library resulted in further enhancements in training efficiency, particularly in single-node scenarios. These optimizations not only improved the performance but also maintained the accuracy and quality of the AI models.

Inference Capabilities of Nvidia H100


The Evolving Landscape of AI Inference


AI inference, the process of running trained neural networks in production environments, is crucial in the AI world. With the rise of generative AI, the demand for high-performance inference capabilities has escalated. The Nvidia H100, powered by the Hopper architecture and its Transformer Engine, is specifically optimized for these tasks, demonstrating its prowess in MLPerf Inference 3.0 benchmarks. This benchmark is significant as it measures AI performance across a range of real-world applications, from cloud computing to edge deployments.

Unprecedented Performance in MLPerf Inference 3.0


The H100 GPUs showcased remarkable efficiency and performance in every test of AI inference in the MLPerf Inference 3.0 benchmarks. The results indicate up to a 54% performance gain from its debut, reflecting the continuous advancements and optimizations in Nvidia’s software and hardware. This level of performance is crucial for generative AI applications, such as those used for creating text, images, and 3D models, where quick and accurate responses are essential.

Optimizations for Enhanced Inference


Nvidia’s commitment to advancing AI inference extends beyond hardware. Software optimizations play a vital role in maximizing performance. These include enhancements in the NVIDIA AI Enterprise software layer, ensuring optimized performance for infrastructure investments. Furthermore, the availability of this optimized software on the MLPerf repository and continuous updates on NGC, Nvidia’s catalog for GPU-accelerated software, make these advancements accessible and beneficial for a wide range of applications.

Versatility Across Applications


The versatility of the Nvidia AI platform is evident in its ability to run all MLPerf inference workloads, catering to various scenarios in both data center and edge computing. This adaptability is crucial as real-world AI applications often employ multiple neural networks of different types, each requiring high-performance inference to deliver real-time responses. The MLPerf benchmarks, backed by leading industry players, provide a transparent and objective measure of this performance, enabling informed decisions for customers and IT decision-makers.

Applications in Real-World Scenarios


H100: A Catalyst for Mainstream AI Applications


The Nvidia H100 represents a significant leap in AI and machine learning capabilities, promising up to 9x faster AI training and 30x faster AI inference than its predecessor, the A100. This dramatic increase in performance has the potential to bring artificial intelligence applications into the mainstream across various industries. With its advanced capabilities, the H100 is much more than just a hardware accelerator; it’s a foundation for a new era of AI applications that are more accessible and powerful than ever before.

The Transformer Engine: Revolutionizing Machine Learning


One of the key factors behind the H100’s speedup is the new Transformer Engine. This engine is specifically designed to accelerate machine learning technologies, particularly those that create large and complex ML models. As these models become increasingly prevalent in the AI landscape, the H100’s Transformer Engine ensures that Nvidia stays at the forefront of these technological advancements. This specialized focus on function-specific optimizations like the Transformer Engine marks a significant shift in how AI hardware is developed, with a clear emphasis on meeting the evolving demands of the industry.

Supercomputer-Class Performance for Businesses


The advancements Nvidia has made with the H100 and the new DGX H100 servers enable businesses of various scales to achieve supercomputer-class performance using off-the-shelf parts. This democratization of high-performance computing power allows more organizations to engage in advanced computing tasks that were previously out of reach due to technological and financial constraints. The expansion of NVLink interconnect, enabling the creation of large-scale, interconnected systems, further amplifies this capability, offering unprecedented computational power in a more accessible format.

H100: Beyond a Traditional GPU


The H100, the ninth generation of Nvidia’s data center GPU, is equipped with more Tensor and CUDA cores at higher clock speeds than the A100. It also features 50MB of Level 2 cache and 80GB of HBM3 memory, providing twice the bandwidth of its predecessor. The addition of new DPX instructions accelerates dynamic programming algorithms in various fields like healthcare, robotics, quantum computing, and data science, showcasing the H100’s versatility beyond traditional GPU applications.

Revolutionizing AI Infrastructure with DGX H100


The DGX H100 represents Nvidia’s fourth-generation AI-focused server system. Packing eight H100 GPUs connected through NVLink, it provides a powerful and scalable solution for delivering AI-based services at scale. The concept of DGX POD and SuperPOD further extends this capability, linking multiple systems to deliver exascale AI performance. These systems not only represent a significant technological achievement but also provide a practical blueprint for organizations looking to leverage AI at a large scale.

Building the World’s Fastest AI Supercomputer


Nvidia plans to combine multiple SuperPODs, amounting to 4,608 H100 GPUs, to build Eos, projected to be the world’s fastest AI supercomputer. This endeavor highlights the critical role of NVLink in these systems, offering a high bandwidth chip-to-chip connectivity solution that significantly surpasses traditional PCIe capabilities. The realization of such a supercomputer underscores the transformative potential of the H100 in pushing the boundaries of AI and high-performance computing.

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AI-driven Applications and Use Cases of Nvidia H100

AI-driven Applications and Use Cases of Nvidia H100

AI-driven Applications and Use Cases of Nvidia H100

Nvidia H100



The realm of accelerated computing is witnessing a revolutionary transformation with the advent of the NVIDIA® H100 Tensor Core GPU, a cornerstone in the NVIDIA Hopper™ architecture. This GPU is not merely an incremental step forward but an order-of-magnitude leap in computing, bridging the gap between ambition and realization in AI and high-performance computing (HPC) domains.

The NVIDIA H100 is designed to address the most complex and data-intensive challenges, making it an ideal powerhouse for AI-driven applications. It stands out with its ability to handle large language models (LLMs) up to 175 billion parameters, thanks to its dedicated Transformer Engine, NVLink, and a substantial 80GB HBM3 memory. This capability enables it to bring LLMs to the mainstream, significantly enhancing the performance of models like GPT-175B by up to 12X over previous generations, even in power-constrained environments.

Furthermore, the H100 facilitates AI adoption in mainstream servers by offering a five-year subscription to the NVIDIA AI Enterprise software suite. This suite provides access to essential AI frameworks and tools, enabling the development of a wide array of AI applications ranging from chatbots and recommendation engines to vision AI.

The GPU’s fourth-generation Tensor Cores and Transformer Engine with FP8 precision contribute to a staggering 4X faster training for models like Llama 2, compared to its predecessors. In terms of AI inference, the H100 extends NVIDIA’s leadership with advancements that boost inference performance by up to 30X, maintaining low latency and high accuracy for LLMs.

Addressing the demands of high-performance computing, the H100 triples the floating-point operations per second (FLOPS) of double-precision Tensor Cores, delivering 60 teraflops of FP64 computing. It’s particularly adept at AI-fused HPC applications, achieving one petaflop of throughput for single-precision matrix-multiply operations without necessitating code changes.

The H100 is not just about raw performance; it also addresses the complexities of data analytics in AI application development. It provides the necessary compute power and scalability to efficiently manage large datasets scattered across multiple servers, which is often a bottleneck in CPU-only server environments.

Moreover, its second-generation Multi-Instance GPU (MIG) technology allows for the partitioning of each GPU into up to seven separate instances. This feature, coupled with confidential computing support, makes the H100 particularly suitable for cloud service provider environments, ensuring secure, multi-tenant usage.

Lastly, NVIDIA Confidential Computing, a built-in security feature of the H100, marks it as the world’s first accelerator with confidential computing capabilities. This feature secures and isolates workloads, ensuring the integrity and confidentiality of data and applications in use, which is crucial for compute-intensive workloads like AI and HPC.

In conclusion, the NVIDIA H100 Tensor Core GPU is a paradigm shift in accelerated computing, driving the next wave of AI and high-performance computing with unparalleled performance, scalability, and security.

Overview of Nvidia H100


Delving into the architecture of the Nvidia H100 GPU reveals a plethora of advancements that redefine the capabilities of AI and HPC applications. At the heart of these innovations is the new fourth-generation Tensor Core technology. These cores significantly enhance matrix computations, crucial for AI and HPC tasks, offering up to 6x faster performance compared to the A100. This leap is partly due to the increased speed per SM, the higher count of SMs, and elevated clock speeds in the H100.

The Transformer Engine is a pivotal component in the H100’s architecture, enabling up to 9x faster AI training and 30x faster AI inference, specifically for large language models, compared to the previous generation A100. This remarkable boost in performance is crucial for applications that require real-time processing and complex computations.

Nvidia has also made significant strides in the realm of connectivity with the new NVLink Network interconnect. This feature allows for efficient GPU-to-GPU communication across up to 256 GPUs, spanning multiple compute nodes, thereby enhancing the scalability and efficiency of large-scale computing tasks.

Another notable feature is the Secure Multi-Instance GPU (MIG) technology, which partitions the GPU into isolated instances, optimizing quality of service for smaller workloads. This aspect of the H100 architecture is crucial for cloud service providers and enterprises that require a high degree of workload isolation and security.

The H100 Tensor Core architecture is a testament to Nvidia’s continued innovation. These cores are specialized for matrix multiply and accumulate (MMA) operations, delivering unmatched performance for AI and HPC applications. The architecture offers double the raw dense and sparse matrix math throughput per SM compared to A100, supporting a range of data types like FP8, FP16, BF16, TF32, FP64, and INT8.

Furthermore, the introduction of new DPX instructions enhances the performance of dynamic programming algorithms, crucial in areas like genomics processing and robotics. These instructions accelerate performance by up to 7x over Ampere GPUs, significantly reducing computational complexity and time-to-solution for complex problems.

Finally, the H100’s memory architecture, featuring HBM3 and HBM2e DRAM subsystems, addresses the growing need for higher memory capacity and bandwidth in HPC, AI, and data analytics. The H100 SXM5 GPU supports 80 GB of fast HBM3 memory with over 3 TB/sec of memory bandwidth, marking a substantial advancement over the A100. Additionally, the L2 cache in H100, being 1.25x larger than that in A100, allows for caching larger portions of models and datasets, enhancing overall performance and efficiency.

Enhancing Large Language Models (LLMs)


The transformation in training large language models (LLMs) brought about by the NVIDIA H100 is monumental. In the contemporary AI landscape, where LLMs such as BERT and GPT are foundational, the size of these models has escalated to trillions of parameters. This exponential growth has extended training times to impractical lengths, often stretching into months, which is unfeasible for many business applications.

The H100 addresses this challenge with its Transformer Engine, a cornerstone of the NVIDIA Hopper architecture. This engine employs 16-bit and the newly introduced 8-bit floating-point precision, alongside advanced software algorithms, drastically enhancing AI performance and capabilities. By reducing the math operations to eight bits, the Transformer Engine facilitates the training of larger networks more swiftly, without sacrificing accuracy. This efficiency is crucial as most AI training relies on floating-point math, traditionally done using 16-bit and 32-bit precision. The introduction of 8-bit operations represents a significant shift in the approach to training LLMs, enabling faster computation while maintaining the integrity of the model’s performance.

Diving deeper into the technicalities, the Transformer Engine utilizes custom NVIDIA fourth-generation Tensor Core technology, designed specifically to accelerate training for transformer-based models. The innovative use of mixed FP8 and FP16 formats by these Tensor Cores significantly boosts AI calculations for transformers, with FP8 operations providing twice the computational throughput of 16-bit operations. This advancement is pivotal in managing the precision of models intelligently to maintain accuracy while benefiting from the performance of smaller, faster numerical formats. The Transformer Engine leverages custom, NVIDIA-tuned heuristics that dynamically choose between FP8 and FP16 calculations, thereby optimizing each layer of a neural network for peak performance and accuracy.

This architectural innovation in the H100 is also evident in its impact on AI workloads beyond LLMs. For instance, in Megatron 530B, a model for natural language understanding, the H100 demonstrates its capability by delivering up to 30x higher inference per-GPU throughput compared to the NVIDIA A100 Tensor Core GPU. This dramatic increase in performance, coupled with a significantly reduced response latency, underscores the H100’s role as an optimal platform for AI deployments. Notably, the Transformer Engine also enhances inference in smaller, highly optimized transformer-based networks, delivering up to 4.3x higher inference performance in benchmarks like MLPerf Inference 3.0, compared to its predecessor, the NVIDIA A100.

AI Adoption in Mainstream Servers


The enterprise adoption of AI has shifted from a niche interest to a mainstream necessity, demanding robust, AI-ready infrastructure. The NVIDIA H100 GPUs, tailored for mainstream servers, exemplify this transition. These GPUs are bundled with a five-year subscription to the NVIDIA AI Enterprise software suite, inclusive of enterprise support. This suite not only simplifies the adoption of AI but also ensures the highest performance levels. With access to comprehensive AI frameworks and tools, organizations are equipped to construct H100-accelerated AI workflows. These workflows span a broad range, from AI chatbots and recommendation engines to vision AI, thereby opening new avenues for innovation and productivity in various sectors.

The H100’s integration into mainstream servers marks a significant leap in AI and HPC capabilities. Featuring fourth-generation Tensor Cores and a Transformer Engine with FP8 precision, the H100 offers up to 4X faster training for advanced models like GPT-3 (175B). The incorporation of fourth-generation NVLink, boasting a 900 gigabytes per second GPU-to-GPU interconnect, along with NDR Quantum-2 InfiniBand networking, ensures accelerated communication across GPU nodes. This network architecture, combined with PCIe Gen5 and NVIDIA Magnum IO™ software, empowers the H100 to deliver efficient scalability. This scalability ranges from small enterprise systems to vast, unified GPU clusters, thus democratizing access to next-generation exascale HPC and AI for a wide array of researchers.

In the realm of business applications, AI’s versatility is unmatched, catering to a diverse range of challenges using various neural network architectures. The H100 stands out as an exceptional AI inference accelerator, offering not just the highest performance but also unparalleled versatility. It achieves this through advancements that boost inference speeds by up to 30X while maintaining the lowest latency. The fourth-generation Tensor Cores in the H100 enhance performance across all precisions, including FP64, TF32, FP32, FP16, INT8, and now FP8, optimizing memory usage and boosting performance, all while ensuring accuracy for LLMs.

Accelerating AI Training and Inference


The NVIDIA H100 represents a paradigm shift in AI training and inference capabilities, setting new benchmarks in performance and efficiency. At the forefront of this advancement is the Transformer Engine, a fusion of software and the cutting-edge NVIDIA Hopper Tensor Core technology. This engine is tailor-made for accelerating transformer model training and inference, a pivotal technology in today’s AI landscape. The transformative aspect of the Transformer Engine lies in its intelligent management of FP8 and 16-bit calculations. This dynamic handling of precision in each layer of a neural network results in up to 9x faster AI training and up to 30x faster AI inference speedups on large language models, a significant leap over the previous generation A100 GPU.

Moreover, the H100’s fourth-generation Tensor Core architecture, along with the innovative Tensor Memory Accelerator (TMA) and other architectural enhancements, collectively contribute to up to 3x faster performance in high-performance computing (HPC) and AI applications. This improvement is not limited to specific tasks but extends across a wide spectrum of AI and HPC use cases, showcasing the H100’s versatility and power.

Delving into the performance specifics, the H100 GPU exhibits exceptional computational capabilities across various floating-point operations. For instance, the peak performance for FP64 Tensor Core operations reaches 60 TFLOPS, while for FP32 it is 60 TFLOPS. The performance further escalates with FP16 and BF16 operations, achieving 120 TFLOPS. Remarkably, the peak performance for FP8 Tensor Core operations reaches a staggering 2000 TFLOPS (or 4000 TFLOPS with the Sparsity feature), showcasing the H100’s prowess in handling complex AI computations with unprecedented efficiency.

Another key aspect of the H100’s architecture is its focus on asynchronous execution, a critical feature for modern GPUs. This capability enables more overlap between data movement, computation, and synchronization, thereby optimizing GPU utilization and enhancing performance. The NVIDIA Hopper Architecture introduces new features like the Tensor Memory Accelerator (TMA) and a new asynchronous transaction barrier, further bolstering the H100’s ability to handle complex, data-intensive AI tasks more efficiently.

Revolutionizing High-Performance Computing (HPC)


The NVIDIA H100 Tensor Core GPU heralds a new era in high-performance computing (HPC), delivering an order-of-magnitude leap in performance over its predecessor, the A100. This ninth-generation data center GPU has been meticulously engineered to enhance strong scaling for AI and HPC workloads, achieving significant improvements in architectural efficiency. In contemporary mainstream AI and HPC models, the H100, equipped with InfiniBand interconnect, provides up to 30x the performance of the A100, marking a generational leap in computing capability. Moreover, the NVLink Switch System interconnect addresses some of the most demanding computing workloads, tripling performance in certain cases over the H100 with InfiniBand.

The NVIDIA Grace Hopper Superchip, featuring the H100, is a groundbreaking innovation for terabyte-scale accelerated computing. This architecture is designed to deliver up to 10x higher performance for large-model AI and HPC applications. It combines the H100 with the NVIDIA Grace CPU, utilizing an ultra-fast chip-to-chip interconnect that provides 900 GB/s of total bandwidth. This design results in 30x higher aggregate bandwidth compared to the fastest current servers, significantly enhancing performance for data-intensive applications.

The H100’s new streaming multiprocessor (SM) includes numerous performance and efficiency enhancements. Key among these is the fourth-generation Tensor Cores, which are up to 6x faster than those in the A100. This includes per-SM speedup, additional SM counts, and higher clock speeds. The introduction of new DPX instructions further accelerates dynamic programming algorithms, such as those used in genomics processing and robotics, by up to 7x over the A100 GPU. Additionally, the H100 achieves 3x faster IEEE FP64 and FP32 processing rates compared to the A100.

Significant architectural advancements in the H100 include new thread block cluster features, enabling efficient data synchronization and exchange across multiple SMs. Furthermore, the distributed shared memory feature allows direct SM-to-SM communications, enhancing data processing efficiency. The introduction of the Tensor Memory Accelerator (TMA) and new asynchronous execution features also contributes to the H100’s superior performance in HPC applications.

The H100’s HBM3 memory subsystem provides a nearly 2x bandwidth increase over the previous generation, with the H100 SXM5 GPU being the world’s first with HBM3 memory, delivering 3 TB/sec of memory bandwidth. The 50 MB L2 cache architecture in the H100 further optimizes data access, caching large portions of models and datasets for repeated access and reducing trips to HBM3.

Another key feature of the H100 is its second-generation Multi-Instance GPU (MIG) technology, providing approximately 3x more compute capacity and nearly 2x more memory bandwidth per GPU instance compared to the A100. This technology is complemented by Confidential Computing support, which enhances data protection and virtual machine isolation in virtualized and MIG environments. The fourth-generation NVIDIA NVLink in the H100 also contributes to its performance, offering a significant bandwidth increase for multi-GPU operations.

The third-generation NVSwitch technology and the new NVLink Switch System interconnect technology in the H100 further enhance its HPC capabilities. These technologies enable up to 32 nodes or 256 GPUs to be connected over NVLink, providing massive bandwidth and computational power, capable of delivering one exaFLOP of FP8 sparse AI compute.

Lastly, PCIe Gen 5 in the H100 provides 128 GB/sec of total bandwidth, enabling the GPU to interface efficiently with high-performing CPUs and SmartNICs or data processing units (DPUs). This integration is pivotal for modern HPC environments, where seamless interaction between different components of the computing infrastructure is essential.

In summary, the NVIDIA H100 GPU introduces a suite of features and technological advancements that significantly improve its performance in HPC applications, making it an ideal solution for tackling the world’s most challenging computational problems.

Enhancing Data Analytics


The NVIDIA H100 Tensor Core GPU represents a substantial advancement in the field of data analytics, an area where computing performance is paramount. Data analytics, especially in the context of AI application development, often becomes a bottleneck due to the extensive time it consumes. This challenge is compounded by the dispersion of large datasets across multiple servers. In such scenarios, traditional scale-out solutions, reliant on commodity CPU-only servers, struggle with a lack of scalable computing performance.

The H100 addresses this challenge head-on by delivering significant compute power coupled with a remarkable 3 terabytes per second (TB/s) of memory bandwidth per GPU. This combination of power and bandwidth, along with scalability features like NVLink and NVSwitch™, empowers the H100 to tackle data analytics tasks with high efficiency. Furthermore, when integrated with NVIDIA Quantum-2 InfiniBand and Magnum IO software, as well as GPU-accelerated Spark 3.0 and NVIDIA RAPIDS™, the H100 forms a part of the NVIDIA data center platform. This platform is uniquely capable of accelerating massive workloads, offering unmatched performance and efficiency levels.

The H100’s memory architecture plays a critical role in its data analytics capabilities. It features HBM3 and HBM2e DRAM subsystems, which are essential as datasets in HPC, AI, and data analytics continue to grow both in size and complexity. The H100 SXM5 GPU supports 80 GB of fast HBM3 memory, delivering over 3 TB/sec of memory bandwidth. This is effectively a 2x increase over the memory bandwidth of the A100. In addition to this, the PCIe H100 offers 80 GB of fast HBM2e with over 2 TB/sec of memory bandwidth. The 50 MB L2 cache in H100, which is 1.25x larger than the A100’s 40 MB L2 cache, further enhances performance by enabling caching of large portions of models and datasets for repeated access, thus improving overall data analytics performance.

Generative AI and data analytics are rapidly evolving fields, and the NVIDIA H100 GPUs have been instrumental in setting several performance records in these areas. For example, in quantitative applications for financial risk management, the H100 GPUs have shown incredible speed and efficiency, setting records in recent STAC-A2 audits. This performance is a testament to the H100’s ability to handle diverse workloads efficiently, including those in data processing, analytics, HPC, and quantitative financial applications.

The NVIDIA H100 is an integral part of the NVIDIA data center platform, built to cater to AI, HPC, and data analytics applications. This platform accelerates over 4,000 applications and is available for a wide range of uses, from data centers to edge computing. The H100 PCIe GPU, with its groundbreaking technology, delivers dramatic performance gains and offers cost-saving opportunities, thereby accelerating a vast array of workloads. Its capabilities in securely accelerating workloads across different data center scales – from enterprise to exascale – make it a versatile solution for data analytics and related applications.

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The Role of Nvidia H100 in Scientific Computing

The Role of Nvidia H100 in Scientific Computing

The Role of Nvidia H100 in Scientific Computing

Introduction to Nvidia H100 and Its Significance in Scientific Computing


The advent of the Nvidia H100 represents a significant milestone in the evolution of scientific computing, marking a transition towards more powerful and efficient processing capabilities. The H100, a product of Nvidia’s persistent innovation, embodies a leap in technology that is reshaping the landscape of High-Performance Computing (HPC) and Artificial Intelligence (AI).

One of the most salient features of the H100 is its ability to handle enormous data sets with unparalleled speed and accuracy. This capability is critical in domains such as weather prediction, drug discovery, and the development of large language models (LLMs) like OpenAI’s GPT-3 and GPT-4. The H100 accelerates the training of these LLMs up to 60 times faster than CPUs, a feat achieved through mixed-precision training. This technique combines high-precision floating-point arithmetic with lower-precision arithmetic, reducing memory requirements and enhancing computation speed.

The prowess of the H100 in parallel processing further cements its role in scientific computing. GPUs, by design, excel at parallel processing, which is essential for handling HPC and AI workloads. This method divides a task into smaller sub-tasks executed simultaneously, enabling GPUs to perform complex calculations much faster than traditional CPUs. The ability to manage parallel processing efficiently is particularly advantageous for AI workloads, where deep learning algorithms necessitate the processing of large data volumes.

At the core of the H100’s performance are its GPU compute cores, known as Streaming Multiprocessors (SMs). Each SM contains several CUDA cores, responsible for executing instructions in parallel, significantly enhancing the GPU’s processing power. The H100, with its 16,896 CUDA cores and 528 Tensor Cores per GPU, is capable of performing tens of teraflops of operations, both in single and double precision. Complementing this is the H100’s unique memory architecture, featuring High Bandwidth Memory (HBM), which delivers high bandwidth, low latency, and high-capacity memory, ideally suited for HPC and AI workloads.

Furthermore, the H100’s Tensor Cores accelerate AI workloads, especially deep learning. These cores are designed for mixed-precision matrix multiplication, providing up to 20 times faster performance than traditional FP32-based matrix multiplication. This acceleration enables faster and more accurate training of deep learning models. Complementing this capability is the NVLink technology, a multi-GPU solution that allows multiple GPUs to collaborate in parallel, solving complex HPC and AI workloads. NVLink provides a high-bandwidth, low-latency connection between GPUs, enhancing data sharing and parallel processing capabilities.

Technological Advancements of Nvidia H100


Advanced Core Architecture


The Nvidia H100, with its ground-breaking core architecture, signifies a new era in scientific computing. At its heart, the H100 features an astonishing 80 billion transistors, leveraging the industry-leading 4-nanometer manufacturing process. This miniaturization not only enhances computational density but also propels efficiency, allowing more calculations per watt of power used. This advancement is crucial in an era where energy efficiency and computational power are paramount in scientific advancements.

Transformer Engine and AI Acceleration


A pivotal innovation in the H100 is the Transformer Engine, designed explicitly for accelerating machine learning technologies, particularly those that underpin large language models (LLMs) like GPT-3 and GPT-4. This engine is a game-changer in AI training and inference, providing up to 30 times faster AI inference for LLMs compared to previous generations. Such acceleration is vital for developing complex AI models that can simulate human-like text generation, offering vast potential for scientific fields reliant on data interpretation and natural language processing.

Enhanced Performance for Scientific Applications


The H100 marks a substantial leap in performance for scientific computing applications. It triples the floating-point operations per second (FLOPS) of double-precision Tensor Cores, delivering a massive 60 teraflops of FP64 computing for HPC. This increase is significant for scientific fields where high precision and vast computational resources are necessary, such as in climate modeling, astrophysics, and molecular dynamics. The ability to process large-scale simulations and models more efficiently opens new horizons for researchers, enabling them to explore more complex systems and phenomena with greater accuracy.

Accelerated Data Analytics


In the realm of data analytics, which forms the backbone of modern scientific research, the H100 offers unparalleled performance. The GPU’s architecture, combined with its 3 terabytes per second memory bandwidth, allows for handling massive datasets that are often scattered across multiple servers. This capability is crucial for AI application development, where large-scale data analysis and processing form the bulk of the workload. The H100’s ability to tackle these datasets efficiently not only speeds up the analytics process but also ensures that insights are derived faster, aiding in quicker decision-making and hypothesis testing in scientific research.

Nvidia H100’s Role in Enhancing Supercomputer Performance


The Nvidia H100, as part of the NVIDIA HGX AI supercomputing platform, marks a significant advancement in the field of supercomputing, especially in AI, simulation, and data analytics. This supercomputing platform, purpose-built for AI and complex simulations, integrates multiple GPUs with extremely fast interconnections and a fully accelerated software stack. Such an architecture is essential to manage and process massive datasets and complex simulations, which are typical in scientific research and AI development. The synergy of NVIDIA GPUs, NVLink, NVIDIA networking, and optimized software stacks provides the highest application performance, significantly reducing the time to insights for complex scientific problems.

The unmatched end-to-end accelerated computing capability of NVIDIA HGX H100 forms the world’s most powerful server configurations. It combines up to eight H100 Tensor Core GPUs with high-speed interconnects, delivering up to 640 gigabytes of GPU memory and 24 terabytes per second of aggregate memory bandwidth. This configuration results in a staggering 32 petaFLOPS of performance, creating an accelerated scale-up server platform for AI and HPC unmatched in the industry. The HGX H100 also includes advanced networking options at speeds up to 400 Gb/s, utilizing NVIDIA Quantum-2 InfiniBand and Spectrum-X™ Ethernet. These features provide the highest AI performance and also incorporate NVIDIA® BlueField®-3 DPUs for enhanced cloud networking, storage, security, and GPU compute elasticity in AI clouds.

In the realm of deep learning training, the H100 GPU demonstrates remarkable performance and scalability. For instance, it offers up to four times higher AI training on GPT-3, a testament to its efficiency in handling large-scale AI models. The combination of fourth-generation NVIDIA NVLink, NVLink Switch System, PCIe Gen5, and Magnum IO™ software enables efficient scalability from small enterprises to extensive, unified GPU clusters. This infrastructure makes the HGX H100 the most powerful end-to-end AI and HPC data center platform, capable of managing the intensive computational demands of modern AI training and simulation workloads.

Furthermore, the H100 GPU excels in deep learning inference, offering up to 30 times higher AI inference performance on the largest models. For example, in the Megatron chatbot inference with 530 billion parameters, the H100 cluster showcased exceptional performance. The H100’s capability to process such massive models with high efficiency underscores its role in advancing AI research and development, particularly in fields that rely on real-time deep learning inference for complex and large-scale models .

Practical Applications in Various Scientific Fields


The Nvidia H100 GPU has ushered in a new era of possibilities across diverse scientific fields, with applications ranging from healthcare to robotics, significantly impacting research methodologies and outcomes.

Enhancing Research in Healthcare

In the realm of healthcare, the H100 GPU is revolutionizing various aspects, from drug discovery to genomics and medical imaging. Its accelerated computing capabilities enable researchers to virtually model millions of molecules and screen hundreds of potential drugs simultaneously. This ability not only reduces costs but also speeds up the time to solution, making the drug discovery process more efficient and effective.

The field of genomics, which requires immense computational power to analyze and interpret complex genetic data, also benefits greatly from the H100 GPU. Its advanced computing power and speed facilitate more in-depth genomic studies, helping to identify rare diseases and advance the journey to precision medicine.

In medical imaging, AI-powered tools enhanced by the H100 GPU act as an additional set of “eyes” for clinicians. These tools aid in quickly detecting and measuring anomalies, thereby improving diagnostics, enhancing image quality, and optimizing clinical workflows.

Impact on Robotics and Data Science


The H100 GPU’s new DPX instructions provide accelerated dynamic programming, crucial in robotics for algorithms like the Floyd-Warshall algorithm. This algorithm is used to find optimal routes for autonomous robot fleets in dynamic environments such as warehouses. Such advancements in dynamic programming algorithms can lead to dramatically faster times-to-solution in logistics routing optimizations, contributing significantly to the efficiency and efficacy of robotics applications.

Advancements in Cardiovascular Medicine


A team from Stanford University has leveraged the power of AI, driven by the capabilities of the H100 GPU, to transform cardiovascular healthcare. By utilizing physics-informed machine learning surrogate models, researchers are generating accurate, patient-specific blood flow visualizations. These visualizations provide a non-invasive window into cardiac studies, crucial for evaluating coronary artery aneurysms, pioneering new surgical methods for congenital heart disease, and enhancing medical device efficacy. Such applications have enormous potential in advancing cardiovascular medicine and offer innovative methods for combating the leading cause of death in the US.

The Nvidia H100 GPU is thus playing a pivotal role in advancing scientific research and applications across various domains. Its capabilities in healthcare, robotics, and cardiovascular medicine demonstrate its transformative impact, enabling more efficient, accurate, and innovative approaches to solving complex scientific challenges.

Virtualization and Data Security: A New Frontier


The Nvidia H100 GPU introduces groundbreaking advancements in virtualization and data security, reshaping the landscape of confidential computing and data protection.

Enhancing Security in Virtualized Environments


Hardware virtualization on the H100 GPU effectively isolates workloads in virtual machines (VMs) from both the physical hardware and each other. This feature is particularly crucial in multi-tenant environments where improved security is vital. Traditional security measures focused on data-in-motion and data-at-rest, leaving data-in-use vulnerable. Nvidia’s introduction of confidential computing addresses this gap, offering robust protection for data and code during processing. This innovation is vital in scenarios where AI training or inference involves sensitive information, such as personally identifiable information (PII) or enterprise secrets.

Confidential Computing with Hardware Virtualization


Nvidia has pioneered confidential computing using hardware virtualization in the H100 GPU. This approach involves performing computation in a hardware-based, attested trusted execution environment (TEE). The H100’s TEE, anchored in an on-die hardware root of trust (RoT), ensures the integrity and confidentiality of code and data. It establishes a chain of trust through a secure and measured boot sequence, secure connection protocols, and the generation of a cryptographically signed attestation report. This mechanism allows users to validate the security of the computing environment before proceeding, ensuring that data remains protected against unauthorized access.

Comprehensive Security Across Hardware, Firmware, and Software


Nvidia has continuously enhanced the security and integrity of its GPUs, with the Hopper architecture bringing significant improvements. The H100 GPU incorporates encrypted firmware, firmware revocation, fault injection countermeasures, and a measured/attested boot. These features form a comprehensive confidential computing solution, safeguarding both code and data. The CUDA 12.2 Update 1 release has made the H100 ready to run confidential computing workloads, marking it as the first GPU capable of such advanced security measures.

Operating H100 GPUs in Confidential Computing Mode


The H100 GPU operates in confidential computing mode with CPUs supporting confidential VMs (CVMs). This setup creates a TEE that extends to the GPU, effectively blocking the GPU from directly accessing the CVM memory. The NVIDIA driver, within the CPU TEE, collaborates with the GPU hardware to securely transfer data to and from GPU memory. This process involves encrypted bounce buffers and signed command buffers and CUDA kernels, ensuring that running CUDA applications in CC-On mode is as seamless as in standard mode. The security protocols are managed transparently, providing a secure and efficient computing environment.

The Hopper Ecosystem and Future Prospects


The Hopper Architecture: A Foundation for Future Innovations


Named after Rear Admiral Grace Hopper, a pioneering computer scientist, the Hopper architecture represents a foundational shift in data center GPU technology. The H100, as the ninth generation of Nvidia’s data center GPU, is a testament to this evolution. It’s not just an increase in the number of Tensor and CUDA cores or the doubling of bandwidth; it’s about redefining what a GPU can do. The H100’s ability to accelerate dynamic programming algorithms across various fields like healthcare, robotics, quantum computing, and data science marks a significant departure from traditional GPU applications.

Transitioning Beyond Traditional GPU Roles


Although still termed a graphics processing unit, the H100’s functionality has evolved far beyond just rendering 3D graphics. This transition is evident in its capacity for GPU virtualization, allowing up to seven isolated instances with native support for Confidential Computing. This evolution reflects a broader trend in high-performance computing where GPUs are no longer just about graphics but are central to complex computational tasks across various scientific domains.

The DGX H100 Server and SuperPOD: Pioneering Exascale AI Performance


The DGX H100 server system, Nvidia’s fourth generation AI-focused server, exemplifies the H100’s capabilities when scaled. Connecting eight H100 GPUs through NVLink, alongside CPUs and Nvidia BlueField DPUs, these servers can be combined to form a DGX POD and even a DGX SuperPOD. The SuperPOD, linking 32 DGX systems with 256 H100 GPUs, delivers one Exaflops of AI performance, a feat previously reserved for the fastest machines in the world. This capability demonstrates the potential of the H100 in driving future AI and scientific computing advancements on an exascale level.


NVLink’s evolution from a GPU interconnect to a versatile tool for chip-to-chip connectivity underlines its significance in Nvidia’s future plans. The H100 supports up to 18 fourth-generation NVLink connections, offering a bandwidth of 900 GB/s. This technology is pivotal in synchronizing multiple systems to work cohesively on complex computing tasks. Nvidia’s announcement to standardize NVLink in all future chips, including CPUs, GPUs, DPUs, and SOCs, and their commitment to releasing a Hopper family CPU called Grace, indicates a strategic direction towards more integrated and efficient computing ecosystems.



The Hopper architecture, embodied in the Nvidia H100, is paving the way for a new era in scientific computing, where GPUs are central to solving some of the most complex and demanding computational challenges. With advancements like the DGX H100 server and the evolution of NVLink, Nvidia is setting the stage for transformative changes in high-performance computing and AI, promising significant impacts on a broad spectrum of scientific and technological fields.

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