Nvidia H100

Nvidia H100
Name	Nvidia H100
Manufacturer	Nvidia
Designed by	Nvidia
Generation	Hopper
Release date	March 2022
Predecessor	Nvidia A100
Fab	TSMC 4N
Transistors	80 billion
Memory	HBM2e / HBM3
Memory bandwidth	Up to 3.35 TB/s
Power	700 W
Interface	PCIe 5.0, SXM

Nvidia H100. The Nvidia H100 Tensor Core GPU is a high-performance computing accelerator designed by Nvidia and based on its Hopper architecture. It was announced in 2022 as the successor to the Nvidia A100, offering transformative performance for artificial intelligence, high-performance computing, and data analytics. Manufactured by TSMC on its advanced 4N process, the H100 is a foundational component for modern AI supercomputers and large-scale data center deployments worldwide.

Overview

The H100 represents a significant leap in GPU design, explicitly engineered to tackle the most demanding workloads in scientific computing and large language model training. Its development was driven by the exponential growth in AI model size and complexity, as seen in projects from organizations like OpenAI and Google DeepMind. The processor is a central element in many of the world's fastest supercomputers, including systems at national laboratories like the U.S. Department of Energy's Oak Ridge National Laboratory and commercial clouds operated by Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

Architecture

Built on the new Hopper architecture, the H100 introduces several groundbreaking technologies. A key innovation is the Transformer Engine, which uses a combination of custom tensor cores, FP8 precision, and intelligent algorithms to dramatically accelerate Transformer model training and inference. The chip features a massive 80 billion transistors and utilizes next-generation HBM2e or HBM3 memory from partners like SK Hynix and Samsung Electronics. Its NVLink interconnect technology, in its fourth generation, enables extremely high-bandwidth communication between multiple GPUs, crucial for scaling applications across systems like the NVIDIA DGX platform.

Performance

Nvidia claims the H100 delivers an order-of-magnitude performance increase over its predecessor, the Nvidia A100, for specific AI workloads. In benchmarks for training massive neural networks, such as those used for natural language processing, the H100 demonstrates vastly improved throughput. Its performance is also transformative for high-performance computing applications in fields like computational fluid dynamics, quantum chemistry simulations, and climate modeling, where its raw floating-point compute power and memory bandwidth are critical. The chip's design supports advanced features like confidential computing for secure multi-tenant environments.

Software and ecosystem

The H100 is fully supported by Nvidia's comprehensive software stack, most notably the CUDA parallel computing platform and associated libraries like cuDNN, NCCL, and the NVIDIA Triton Inference Server. This ecosystem is essential for developers at companies like Meta and Tesla to optimize their applications. Frameworks such as PyTorch and TensorFlow are deeply integrated with H100's capabilities, particularly the Transformer Engine. The chip is also a primary target for Nvidia's Omniverse platform for 3D simulation and its suite of AI enterprise software.

Applications

The primary application for the H100 is in training and deploying frontier artificial intelligence models, including generative AI systems like GPT-4 and Stable Diffusion. It is indispensable for pharmaceutical research in companies like Genentech for drug discovery, and for autonomous vehicle development at firms like Waymo. Within scientific research, it powers simulations for projects at CERN and NASA, and it accelerates financial modeling for institutions like JPMorgan Chase. The GPU is also pivotal for real-time recommendation systems used by Netflix and Alibaba Group.

Market and availability

The H100 entered volume production in late 2022, with availability primarily through Nvidia's partners and its own NVIDIA DGX systems. High demand from major cloud computing providers, including Oracle Cloud and IBM Cloud, as well as from sovereign nations and private AI research labs, has led to significant supply constraints. The processor is a critical component in specialized data center infrastructure, often deployed in clusters like the NVIDIA HGX platform. Its market success has significantly influenced the competitive strategies of rivals like AMD with its Instinct series and various AI accelerator startups.

Category:Nvidia graphics processing units Category:Graphics processing units Category:Artificial intelligence accelerators Category:2022 in computing