Introduction to AI and Deep Learning Capabilities of the Nvidia H100
AI and Deep Learning: A Transformative Journey
In the ever-evolving landscape of technology, artificial intelligence (AI) and deep learning stand as beacons of progress, fundamentally altering how we interact with and perceive the world around us. The advances in these fields are not just technological feats; they are reshaping business processes and societal norms alike. In 2023, the progression of AI is characterized by a few notable trends and achievements, each signaling a significant leap in our capacity to harness the power of machine learning and neural networks.
One of the most striking trends is the rise of Automated Machine Learning (AutoML). This advancement simplifies and streamlines the once complex and labor-intensive processes of labeling data and tuning neural network architectures. AutoML embodies a shift towards more efficient and accessible AI, democratizing the development process and reducing reliance on extensive manually labeled data. Such innovations are not just about technological improvement; they redefine the economic and operational landscape of AI, making it more cost-effective and agile.
Another transformative trend is AI-enabled conceptual design. Traditionally confined to data analytics, image processing, and linguistic applications, AI is now venturing into creative realms. With models like OpenAI’s DALL·E and CLIP, AI is transcending its conventional boundaries, blending language and imagery to generate novel visual concepts. This leap into creative industries hints at a future where AI’s influence pervades every sector, from fashion to architecture, revolutionizing how we conceive and create.
Moreover, the advent of multi-modal learning marks a significant evolution in AI’s capabilities. This approach integrates multiple data types within a single machine learning model, enhancing AI’s applicability across diverse fields. For instance, in healthcare, multi-modal AI can amalgamate various forms of patient data, from visual lab results to clinical documents, offering more nuanced and comprehensive insights. The implication is profound, promising improved medical diagnoses and treatments. This leap in AI’s functional versatility underscores the technology’s growing sophistication and its potential to revolutionize industries far beyond its traditional scopes.
As AI continues to advance, the focus is also shifting towards models capable of achieving multiple objectives. This evolution reflects a maturing understanding of AI’s potential and its application in complex, real-world scenarios. Instead of targeting singular metrics, these multi-task models are designed to balance various objectives, aligning with broader business goals and societal values, such as sustainability and ethical considerations. This trend illustrates a more holistic approach to AI, acknowledging its multifaceted impact on business and society.
As we delve deeper into the AI and Deep Learning Capabilities of the Nvidia H100, it’s crucial to recognize these broader trends and advancements. They not only contextualize the technological prowess of the H100 but also underscore the transformative role of AI and deep learning in shaping our world. The journey of AI is not merely a narrative of technological progress; it’s a story of human innovation and its far-reaching implications across industries and societies.
Overview of Nvidia H100
Unveiling the Powerhouse: Nvidia H100
The Nvidia H100 represents a quantum leap in data center technology, a culmination of Nvidia’s relentless pursuit of excellence in the realm of artificial intelligence and deep learning. Launched at GTC 2022, the H100 is the centerpiece of Nvidia’s new Hopper architecture, a testament to the company’s commitment to pioneering the future of AI hardware. With 80 billion transistors and an innovative dual-core design, the H100 stands out not only as a significant upgrade over its predecessor, the A100, but also as a beacon of AI’s future potential.
This hardware accelerator is much more than a mere improvement; it redefines the possibilities of AI training and inference. Promising up to nine times faster AI training and thirty times quicker AI inference in popular machine learning models, the H100 is not just a piece of technology—it’s the cornerstone of an IT revolution, making advanced AI applications more accessible and mainstream than ever before.
Central to the H100’s breakthrough performance is the Transformer Engine, a marvel of engineering designed to supercharge machine learning technologies. This engine is pivotal in accelerating the creation of large and complex ML models, reflecting Nvidia’s deep understanding of industry needs and its dedication to functional optimizations that matter to its customers.
On the server front, the H100 enables businesses to achieve supercomputer-class performance using off-the-shelf components. This democratization of power is further enhanced by the DGX H100 servers, which incorporate the proprietary NVLink interconnect, allowing for the creation of expansive, data center-sized GPUs. This technological leap is not just about raw power; it’s about making supercomputing capabilities more accessible and practical for a broader range of businesses and applications.
The H100’s impressive technical specifications include a rich array of Tensor and CUDA cores operating at higher clock speeds than its predecessor, and a doubled bandwidth reaching 3 TB/sec. It also introduces new DPX instructions that can significantly accelerate dynamic programming algorithms in diverse fields like healthcare, robotics, quantum computing, and data science.
Despite its designation as a graphics processing unit, the H100 transcends traditional GPU roles, focusing primarily on AI and deep learning applications. Its ability to be divided into up to seven isolated instances, coupled with native support for Confidential Computing, marks it as the first multi-instance GPU capable of protecting data not only during storage or transfer but also in use.
The DGX H100, Nvidia’s fourth-generation AI-focused server system, further cements the H100’s position at the forefront of AI technology. This system, when interconnected in a DGX SuperPOD configuration, can deliver an astonishing one Exaflops of AI performance, a feat reserved for the world’s fastest machines just a few years ago.
In summary, the H100’s capabilities are not just a step forward; they represent a significant leap in AI and deep learning technology. Its introduction marks a pivotal moment in the journey of AI, ushering in a new era of possibilities and applications across various industries.
Deep Learning Inference and Versatility of the Nvidia H100
Real-Time Deep Learning Inference: A Game-Changer
The Nvidia H100’s Transformer Engine, a groundbreaking innovation in the AI industry, represents a monumental shift in deep learning capabilities. Initially integrated within the Nvidia Hopper architecture, this engine has been pivotal in the exponential acceleration of AI performance, particularly in training large models, some with trillions of parameters, within a drastically reduced timeframe. These large models, fundamental to applications like natural language processing (NLP), computer vision, and drug discovery, have historically been hindered by prolonged training times. The Transformer Engine, however, mitigates this challenge by utilizing 16-bit floating-point precision and an added 8-bit floating-point data format, coupled with advanced software algorithms. This technological synergy not only speeds up AI performance but also preserves accuracy, a critical aspect in AI applications.
The Technical Mastery Behind Transformer Engine
Delving deeper into its technical architecture, the Transformer Engine leverages custom Nvidia fourth-generation Tensor Core technology, designed specifically to accelerate training for transformer-based models. These Tensor Cores apply mixed FP8 and FP16 formats, effectively doubling the computational throughput compared to 16-bit operations. By intelligently managing precision, the Engine maintains model accuracy while enhancing performance. This per-layer statistical analysis optimizes the precision for each layer of a model, striking a balance between high performance and accuracy. The Nvidia Hopper architecture complements this by tripling the floating-point operations per second, compared to prior generations. This synergy between the Transformer Engine, Tensor Cores, and the Hopper architecture enables significant speedups for high-performance computing (HPC) and AI workloads.
Transforming AI Training and Inference
The cutting-edge AI research today, epitomized by models like Megatron 530B, underscores an unrelenting demand for AI compute power. The H100 addresses these needs by offering the computational might and high-speed memory essential for handling such large models. Innovations like the Transformer Engine enable a remarkable 9x increase in training throughput and a reduction in training time from days to mere hours. Additionally, the Engine supports inference operations without data format conversions, a significant enhancement over previous models that required conversion to INT8. This allows for more streamlined and efficient deployments, even in memory-constrained environments.
Elevating Inference Performance
In terms of inference performance, the H100 showcases a dramatic improvement over its predecessor. On Megatron 530B, the H100 delivers up to 30x higher per-GPU throughput compared to the A100, with minimal response latency, positioning it as an optimal platform for diverse AI deployments. This enhanced capability is not limited to large-scale models; the H100 also boosts inference on smaller, already optimized transformer-based networks. In benchmarks like MLPerf Inference 3.0, the H100 exhibited up to 4.3x higher inference performance, underscoring its proficiency across a spectrum of AI applications.
Optimization for Large Workloads in the Nvidia H100
Revolutionizing Exascale Computing
The Nvidia H100 Tensor Core GPU ushers in a new era in accelerated computing, redefining the benchmarks for performance, scalability, and security across diverse workloads. A cornerstone of this revolution is the Nvidia NVLink® Switch System, which allows up to 256 H100 GPUs to be interconnected, thereby accelerating exascale workloads to unprecedented levels. The inclusion of a dedicated Transformer Engine enables the H100 to tackle trillion-parameter language models, pushing the boundaries of what is possible in conversational AI and making large language models (LLMs) 30 times faster than previous generations.
Streamlining Large Language Model Processing
For LLMs of up to 175 billion parameters, the PCIe-based H100 NVL, equipped with NVLink bridge, leverages its Transformer Engine, NVLink, and 188GB HBM3 memory to deliver optimum performance. This configuration ensures seamless scaling across any data center, bringing high-parameter LLMs into the mainstream. Servers armed with H100 NVL GPUs achieve a performance increase of up to 12 times over Nvidia DGX™ A100 systems while maintaining low latency, even in power-constrained environments.
Enhancing AI Training and Communication
The H100 is equipped with fourth-generation Tensor Cores and the Transformer Engine, featuring FP8 precision. This setup offers up to four times faster training for models like GPT-3 (175B), compared to previous generations. Additionally, it combines fourth-generation NVLink, providing 900 GB/s of GPU-to-GPU interconnect, NDR Quantum-2 InfiniBand networking for accelerated GPU communication across nodes, PCIe Gen5, and NVIDIA Magnum IO™ software. This powerful combination enables scalable, efficient AI training from small enterprise systems to large, unified GPU clusters.
Democratizing Access to Trillion-Parameter AI
Deploying H100 GPUs at a data center scale brings next-generation exascale HPC and trillion-parameter AI within reach of a broader range of researchers. This democratization of advanced computing resources is a significant milestone, allowing more institutions and companies to engage in cutting-edge AI research and development.
Advancing AI Inference and Precision
The H100 also extends Nvidia’s leadership in AI inference, introducing advancements that boost inference speeds by up to 30 times while achieving the lowest latency. Its fourth-generation Tensor Cores accelerate computations across all precisions, including FP64, TF32, FP32, FP16, INT8, and now FP8. This enhancement not only reduces memory usage but also increases performance, ensuring accuracy in LLMs and other AI applications.
Benchmarking Performance and Industry Impact of the Nvidia H100
Setting New Benchmarks in AI Performance
The introduction of the Nvidia H100 has set a new standard in AI performance, particularly in handling large language models (LLMs) powering generative AI. This was exemplified in a landmark industry-standard benchmark where a cluster of 3,584 H100 GPUs at CoreWeave, a cloud service provider, completed a massive GPT-3-based benchmark in a mere 11 minutes. This feat not only showcases the raw power of the H100 GPUs but also their ability to handle large-scale AI tasks efficiently and rapidly.
Facilitating Advanced Language Model Development
Inflection AI leveraged the power of the H100 to develop an advanced LLM for its first personal AI, known as Pi. This deployment reflects the capability of the H100 GPUs to enable the creation of sophisticated AI models that can interact with users in simple, natural ways. The ambition to build one of the largest computing clusters in the world using Nvidia GPUs further underscores the trust and reliance placed in the H100’s capabilities.
Comprehensive Performance Across Diverse AI Applications
In terms of versatility, the H100 GPUs delivered outstanding performance across all benchmarks in the latest MLPerf training benchmarks, including LLMs, recommenders, computer vision, medical imaging, and speech recognition. Their capability to run all eight tests demonstrates the comprehensive nature of the Nvidia AI platform. This all-encompassing performance is crucial for training, a task often conducted at scale with many GPUs working together. The H100 GPUs set new performance records for AI training at scale, reflecting their adaptability to various AI workloads.
Impact on Cloud Service Providers
CoreWeave, using H100 GPUs, delivered performance comparable to what Nvidia achieved with an AI supercomputer in a local data center. This parity is significant for cloud service providers, as it illustrates the potential for deploying high-end AI solutions in cloud environments. MLPerf’s updated benchmarks for recommendation systems, which Nvidia was the only company to submit results for, further emphasize the H100’s suitability for modern AI challenges faced by cloud service providers.
Broad Industry Ecosystem and Future Prospects
The Nvidia H100’s performance is backed by the industry’s broadest ecosystem in machine learning, with nearly a dozen companies submitting results on the Nvidia platform. This includes major system makers like ASUS, Dell Technologies, and Lenovo. The broad participation in these benchmarks indicates a widespread confidence in the H100’s capabilities, applicable both in cloud environments and in local data centers. Such an ecosystem allows users to be confident in their choice of Nvidia AI for a range of applications, from computer vision to generative AI and recommendation systems.
Benefits for Users of the Nvidia H100
Transforming Enterprise AI with H100 CNX
The Nvidia H100 CNX stands as a high-performance package that marries the prowess of the Nvidia H100 with the advanced networking capabilities of the Nvidia ConnectX-7 SmartNIC. Designed for mainstream data center and edge systems, this combination offers unprecedented performance for GPU-powered and I/O intensive workloads. The H100 CNX is particularly suited for enterprises seeking to deploy high-performance AI applications without the overhead of custom-built systems.
Design Innovations Enhancing Data Transfer
A key design benefit of the H100 CNX is its integration of the GPU and network adapter through a direct PCIe Gen5 channel. This architecture provides a high-speed path for data transfer between the GPU and the network, using GPUDirect RDMA to eliminate bottlenecks typically encountered in standard PCIe devices. This design innovation is crucial for applications requiring rapid and efficient data transfer, such as real-time AI processing and large-scale data analytics.
Cost-Effective and Efficient Performance
The convergence of the GPU and SmartNIC onto a single board not only achieves higher performance levels but also brings about significant savings in hardware costs. This integration results in improved space and energy efficiency, a vital consideration for data centers aiming to optimize their operations while reducing their environmental impact.
Scalable AI Training and 5G Applications
The H100 CNX’s design enables balanced architecture and scalable performance, crucial for multinode AI training and 5G applications. In multinode training, the H100 CNX overcomes typical performance limitations in data center networks, allowing for efficient data transfer between GPUs on different hosts. This facilitates large-scale AI model training and deployment, crucial for advanced AI applications and research. Additionally, the H100 CNX excels in 5G signal processing, providing a robust platform for running high-performance 5G applications. The integrated design also supports accelerating edge AI over 5G, enabling AI processing in edge devices like video cameras and industrial sensors. This capability is particularly beneficial for sectors like telecommunication and smart city development, where quick and reliable data processing is essential.