What is the Nvidia H100?

What is the Nvidia H100?

The NVIDIA H100 is a high-performance Tensor Core GPU designed for data center and cloud-based applications, optimized for AI workloads. It offers unprecedented performance, scalability, and security, making it a game-changer for large-scale AI and HPC workloads. The H100 is built on the cutting-edge NVIDIA Hopper GPU architecture, which introduces several innovations that make it more powerful, efficient, and programmable than any previous GPU.

Key features of the NVIDIA H100 include:

• Architecture: The H100 is built on the 4 nm process and features 14,592 CUDA cores, 456 tensor cores, and 80 GB of HBM2e memory.
• Performance: The GPU operates at a frequency of 1,095 MHz, which can be boosted up to 1,755 MHz, and has a memory running at 1,593 MHz.
• Connectivity: The H100 supports PCIe Gen 5 and NVLink for ultra-high bandwidth and low-latency communication with other GPUs and devices.
• Multi-Instance GPU (MIG): The H100 can be partitioned into right-sized slices for workloads that don't require a full GPU, allowing multiple workloads to run on the same GPU for optimal efficiency.
• AI Acceleration: The H100 includes the new Transformer Engine and NVIDIA AI Enterprise, which optimizes the development and deployment of accelerated AI workflows.
• Price: The flagship H100 GPU is priced at around $30,000 (average).

The NVIDIA H100 is primarily used in data centers and for AI workloads, offering a significant performance leap compared to previous generations. It is widely used in large-scale AI and HPC applications, powering data centers with an order of magnitude speedup over the prior NVIDIA NVLink.

Key Features and Specifications

The H100 GPU has a maximum thermal design power (TDP) of up to 700W, which is configurable to 300-350W. It supports up to 7 Multi-Instance GPUs (MIGs) at 10GB each. The H100 GPU features fourth-generation Tensor Cores, which perform matrix computations faster and more efficiently than ever before. This allows the H100 to handle a broader range of AI and HPC tasks with ease.

The H100 GPU delivers up to 5x faster AI training and 30x faster AI inference speedups on large language models compared to the previous A100 generation. It also accelerates exascale workloads with a dedicated Transformer Engine for trillion-parameter language models.

The H100 GPU can be partitioned down to right-sized Multi-Instance GPU (MIG) partitions for smaller jobs. With Hopper Confidential Computing, this scalable compute power can secure sensitive applications on shared data center infrastructure.

The NVIDIA Hopper architecture, on which the H100 is built, delivers unprecedented performance, scalability, and security to every data center. Hopper builds upon prior generations with new compute core capabilities, such as the Transformer Engine, and faster networking to power the data center with an order of magnitude speedup over the prior generation.

Performance

The H100 GPU has set new records on all eight tests in the latest MLPerf training benchmarks, excelling on a new MLPerf test for generative AI. It delivered the highest performance on every benchmark, including large language models, recommenders, computer vision, medical imaging, and speech recognition.

The H100 GPU also set new at-scale performance records for AI training. Optimizations across the full technology stack enabled near-linear performance scaling on the demanding LLM test as submissions scaled from hundreds to thousands of H100 GPUs.

Use Cases

The H100 GPU is suitable for a wide range of use cases. It is ideal for applications that require high-performance computing, such as complex AI models and scientific research. It is also a perfect match for PCIe expansions and GPU servers.

The H100 GPU is particularly effective for generative AI and large language models (LLMs). It has been used to set new records in the MLPerf training benchmarks, demonstrating its superior performance in these areas.

Conclusion

The NVIDIA H100 Tensor Core GPU represents a significant step forward in GPU technology. With its advanced architecture, fourth-generation Tensor Cores, and the ability to deliver lightning-fast AI training and inference speedups on large language models, it is one of the most powerful, programmable, and power-efficient GPUs to date. It is an excellent choice for organizations that require high-performance computing capabilities, making it a game-changing solution for those working with complex AI models.