As artificial intelligence (AI) and machine learning (ML) advance rapidly, the incorporation of GPUs has become indispensable for AI achievements. NVIDIA, an AI and high-performance computing (HPC) trailblazer, has introduced the groundbreaking NVIDIA H100 GPU, generating unparalleled enthusiasm in the tech industry.
This article offers insights into the performance and scalability of the NVIDIA H100 GPU, highlighting the rationale and advantages of enhancing your AI/ML infrastructure with this cutting-edge release by NVIDIA.
Powerful Computing Performance
The NVIDIA H100 GPU is constructed on the NVIDIA Hopper architecture, delivering numerous substantial performance enhancements when compared to its forerunner, the A100. Leveraging its fourth-generation Tensor Cores, the H100 effectively doubles the computational throughput of each Streaming Multiprocessor (SM) in contrast to the A100, supporting data types such as TF32, FP32, and FP64, thereby accelerating calculations.
Beyond an augmented SM count, the H100 boasts elevated clock frequencies, functioning at 1830 MHz for the SXM5 form factor and 1620 MHz for the PCIe version. These enhancements yield significantly superior performance compared to the A100, ensuring a more seamless and responsive experience within the domain of machine learning.
Moreover, the H100 ushers in a novel FP8 data type, quadrupling the computation rates of FP16 on the A100. Coupled with the Transformer Engine of the NVIDIA Hopper architecture, the H100 can intelligently govern and dynamically select between FP8 and 16-bit computations, amplifying performance while upholding precision, which proves especially advantageous for transformer-based models.
Both users and industry-standard benchmarks agree: NVIDIA H100 Tensor Core GPUs establish themselves as the leaders in AI performance, particularly in the context of large language models (LLMs) that drive Generative AI.
H100 GPUs have set records on all 8 assessments featured in the recent MLPerf training benchmarks (including large language models, recommenders, computer vision, medical imaging and speech recognition), demonstrating remarkable prowess in a new MLPerf test designed for Generative AI. This exceptional performance is consistently delivered, whether considering per-accelerator performance or scaling up in extensive server configurations.
High Scalable Performance
In AI training, scalability is crucial, and H100 GPUs proved their excellence by setting new performance records at scale on every MLPerf test. The LLM test, in particular, showcased near-linear performance scaling as submissions increased from hundreds to thousands of H100 GPUs.
The NVIDIA H100 GPU has remarkable scalability to cater to the expanding requirements of deep learning. It leverages NVIDIA's fourth-generation NVLink technology, which ensures direct GPU interconnectivity, significantly boosting bandwidth and communication speed compared to PCIe lanes. Equipped with 18 NVLink interconnections, the H100 delivers a total bandwidth of 900 GB/s, a substantial upgrade from the A100's 600 GB/s.
Furthermore, the H100 takes advantage of NVIDIA's third-generation NVSwitch technology to facilitate rapid GPU communication within a single node and across nodes. This technology enables the H100 to provide an all-to-all communication bandwidth of 57.6 TB/s, making it ideal for large-scale distributed training and model parallelization.
Diverse Use Case
The NVIDIA H100 Tensor Core GPU caters to a wide range of applications in artificial intelligence and deep learning. Large models with high structured sparsity, such as language and vision models, see up to a 4x boost in training speed compared to the A100. This optimization for structured sparsity makes the H100 especially suitable for large transformer-based models.
Large-scale data parallelization significantly benefits from the H100's NVLink and NVSwitch technologies, offering a 4.5x increase in all-reduce throughput in setups with 32 nodes and 256 GPUs. This enhancement ensures efficient GPU communication, making it perfect for distributed training of complex models.
Lastly, the H100 excels in model parallelization, a crucial use case for advanced models that require parallelization across multiple GPUs or nodes. Thanks to its NVSwitch system, the H100 provides exceptional performance, as demonstrated by a 30x speedup in inference compared to an A100 system with the same number of GPUs when running the Megatron Turing NLG model.
Conclusion
The NVIDIA H100 Tensor Core GPU is a game-changer for AI and machine learning. With fourth-generation Tensor Cores for outstanding performance, along with remarkable scalability through NVLink and NVSwitch technologies, and innovative features like the Transformer Engine and FP8 data type, the H100 pushes the envelope of high-performance computing. Whether you're working with large language models, vision models, or a multitude of AI applications, the H100 is a must-have tool for researchers and businesses aiming to advance the frontiers of AI.
At GreenNode, we provide cutting-edge NVIDIA GPUs (H100, GH200, L40S, A40), ensuring superb performance across a wide range of GPU-intensive tasks, from AI and ML to Deep Learning and VFX Rendering.
Learn more about our service here.