What is the NVIDIA H100 GPU?
NVIDIA H100 GPU is an advanced GPU, which is capable of processing Large Language Models (LLMs) training and deployment significantly faster. These advanced AI models have super-large AI workloads and need a lot of computing power. It is a key tool for high-performance computing (HPC) and enterprise AI.
It is optimized to run transformer models and has a new NVIDIA Hopper architecture. Most of the LLMs are founded on these models. It has a huge amount of cores, memory bandwidth and capacity.
The H100 is not designed for gaming and graphics, as the focus of the server is heavy computations and solving large AI issues. It lies at the core of the modern AI supercomputers.
H100’s Features for AI
The H100 features enable it to be immensely powerful in training and running huge AI models.
The Transformer Engine
The important innovation in the H100 is the Transformer Engine. The transformer models are the foundation of the modern LLMs, including GPT-4.
It is extremely time-consuming to train these models, and the Transformer Engine speeds up this process immensely.
It intelligently switches between two data formats. Moreover, it uses 8-bit floating-point (FP8) and 16-bit floating-point (FP16) precision in a layer-by-layer basis and gives a large speed advantage to the H100.
It delivers 9 times higher training throughput and 30 times faster inference of the LLMs compared to the previous generation model (A100).
This engine helps in accelerating training with no cut-off on accuracy.
The fourth-generation Tensor Cores
The H100 features the fourth-generation Tensor Cores, which form the basis of the AI performance of the H100. Compared to the older cores, these cores are much faster. They can perform matrix multiplication at a very fast rate. It is the main task of deep learning.
These new cores can support a variety of data formats, including the new FP8 format. Thus, the H100 is much more versatile. They are able to handle a wider range of AI and HPC applications.
The raw throughput is also increased by the new Tensor Cores. They can perform 6x higher throughput in matrix operations than the A100 under certain workloads.
High-Bandwidth Memory and Interconnect
HBM3 memory is installed in the H100, being ultra-fast and boasting of massive memory bandwidth of up to 3.35 TB/s. The high memory bandwidth is essential in the training of LLM as it requires a lot of data to be transferred within a short time.
H100 also features a new NVLink Switch System, which links two or more H100 GPUs. In such a manner, this system will allow using 256 GPUs at the same time. Each H100 GPU supports up to 900 GB/s of NVLink bandwidth – perfect for efficient scaling across hundreds of GPUs for very large AI clusters.
Comparison of NVIDIA H100 and NVIDIA A100
The H100 is more advanced than the A100 in many aspects. It has increased memory and bandwidth. Transformer Engine of H100 is an innovation in LLMs. Further, multi-GPU set-ups are also much more efficient with NVLink bandwidth.
| Feature | NVIDIA A100 | NVIDIA H100 |
| Architecture | Ampere | Hopper |
| Process Node | 7nm | 4nm (TSMC 4N) |
| Transistors | 54 billion | 80 billion |
| Memory Capacity | 40GB or 80GB HBM2e | 80GB HBM3 |
| Memory Bandwidth | Up to 2.0 TB/s | Up to 3.35 TB/s |
| Tensor Cores | 3rd Gen | 4th Gen (with FP8) |
| LLM Training Speed | Standard | Up to 9x faster |
| LLM Inference Speed | Standard | Up to 30x faster |
| Transformer Engine | No | Yes |
| NVLink Bandwidth | 600 GB/s | 900 GB/s |
Cantech’s H100 GPU Servers
Cantech offers high-end services and functionalities with our NVIDIA H100 GPUs. Our solutions are designed to help you efficiently train and deploy the largest AI models. It includes the most recent and advanced H100 GPU technology to achieve real-time data analytics, HPC and natural language processing.
H100 GPU clusters are also available on the cloud. You can single-handedly acquire a single GPU or a cluster of many.
Furthermore, our infrastructure is performance-optimized and has high-speed networking to connect the GPUs to each other to allow you to train models that require more than one GPU. It suits well for the big enterprises. You are also able to scale your resources easily.
We offer a very competitive pricing framework. Our team of professionals is ever on-call to assist 24/7 with extensive H100 GPU knowledge. They will help you with the setup of your environment and optimization of your H100 servers to match your own project.
We are also providing fast deployment, continuous maintenance and advanced hardware customization services. We make sure that you do not need to spend time worrying about infrastructure and focus on your AI projects.
Conclusion
NVIDIA H100 is a GPU server that is among the leaders in the advanced AI use case. It is built for the heaviest load using its new Hopper architecture and Transformer Engine, which is revolutionary. They provide the industry-leading performance in terms of HPC and LLM training workloads. The H100 is a breakthrough AI platform. It will help companies to develop bigger and more powerful AI models. The H100 is the model that one should select in case one is interested in a serious performance of AI.
FAQs
What is a Large Language Model (LLM)?
LLM is a large AI model which is trained on a massive amount of text data. It is able to comprehend and produce language similar to a human being. Examples include models such as GPT-4 and LLaMA. It needs huge computing power in order to train these models. H100 is developed based on this task.
How does the H100’s Transformer Engine work?
The Transformer Engine is a special hardware feature that helps to accelerate transformer models. It works by dynamically choosing between FP8 and FP16 data formats. It uses the faster FP8 format for most calculations. Also, it switches to the more precise FP16 when needed for a huge performance boost while maintaining accuracy.
Can the H100 be used for high-performance computing (HPC) jobs?
Yes. The H100 is excellent for HPC. It has high FP64 performance for scientific simulations and new DPX instructions that speed up dynamic programming algorithms. These are used in things like genomics and route optimization. All in all, H100 is a versatile accelerator.
Why is a H100 GPU’s memory bandwidth important for LLMs?
LLM models have billions of parameters. They need to move vast amounts of data between the memory and the cores. A higher memory bandwidth means this data transfer is faster and prevents a bottleneck. Also, the GPU can stay busy with calculations. The H100’s high bandwidth is a key factor in its speed.
