The Unparalleled Power of NVIDIA H100 for Deep Learning
With the remarkable progress in machine learning, the use of GPUs has become essential for success in the field of artificial intelligence. NVIDIA, a pioneer in AI and high-performance computing, has recently unveiled the NVIDIA H100 GPU, sparking unprecedented excitement. This blog provides an overview of the performance and scalability of the NVIDIA H100 GPU, shedding light on the reasons and benefits of upgrading your ML infrastructure with this latest release from NVIDIA.
Unmatched Computing Performance
The NVIDIA H100 GPU is built on the NVIDIA Hopper architecture, offering several major performance improvements compared to its predecessor, the A100. With its fourth-generation Tensor Cores, the H100 doubles the computational throughput of each streaming multiprocessor (SM) compared to the A100, supporting data types like TF32, FP32, and FP64 for faster and more precise calculations.
In addition to an increased number of SMs, the H100 offers higher clock frequencies, operating at 1830 MHz for the SXM5 form factor and 1620 MHz for the PCIe version. These improvements result in significantly higher performance compared to the A100, providing a smoother and more responsive experience in the realm of machine learning.
The H100 also introduces a new FP8 data type, quadrupling the calculation rates of FP16 on the A100. Combined with the Transformer Engine of the NVIDIA Hopper architecture, the H100 can intelligently manage and dynamically choose between FP8 and 16-bit calculations, enhancing performance while maintaining high accuracy, particularly beneficial for transformer-based models.
The NVIDIA H100 GPU also offers impressive scalability to meet the growing demands of deep learning. Leveraging NVIDIA’s fourth-generation NVLink technology, the H100 ensures direct interconnectivity between GPUs, significantly increasing bandwidth and improving communication speed compared to PCIe lanes. With 18 NVLink interconnections, the H100 delivers a total bandwidth of 900 GB/s, a substantial improvement over the A100’s 600 GB/s.
The H100 also capitalizes on NVIDIA’s third-generation NVSwitch technology to facilitate fast communication between GPUs within a single node and between nodes. With this technology, the H100 offers an all-to-all communication bandwidth of 57.6 TB/s, ideal for large-scale distributed training and model parallelization.
Diverse Use Cases
The NVIDIA H100 Tensor Core GPU offers a diverse range of use cases for artificial intelligence and deep learning. Large models with high structured sparsity, such as language and vision models, experience up to 4x acceleration during training compared to the A100. The optimization of Tensor Cores for models with high structured sparsity makes the H100 ideal for large transformer-based models.
Large-scale data parallelization greatly benefits from the NVLink and NVSwitch technologies of the H100, offering a 4.5x increase in all-reduce throughput in configurations with 32 nodes and 256 GPUs. This improvement ensures efficient communication between GPUs, ideal for distributed training of complex models.
Lastly, model parallelization is a major use case for the H100, as many advanced models no longer fit on a single GPU, necessitating parallelization across multiple GPUs or GPU nodes. The NVSwitch system of the H100 enables exceptional performance in this context, as evidenced by the inference with the Megatron Turing NLG model, providing a 30x speedup compared to a reference A100 system with the same number of GPUs.
The NVIDIA H100 Tensor Core GPU offers unparalleled power for artificial intelligence and deep learning. With its superior performance featuring fourth-generation Tensor Cores, impressive scalability with NVLink and NVSwitch technologies, and advanced features like the Transformer Engine and FP8 data type, the H100 redefines the boundaries of high-performance computing. With its benefits for large language models, vision models, and various applications, the H100 is an essential asset for researchers and businesses seeking to push the boundaries of artificial intelligence.