The Nvidia H100 is a high -end GPU specially designed for AI, the Deep Learning and HPC applications. It is based on Hopper architecture and incorporates powerful fourth generation Tensor Cores which offer exceptional performance. Thanks to its enormous calculation capacity, the NVIDIA H100 is ideal for the training of complex neural networks, the intensive cloud workloads and sophisticated HPC simulations.
What are the characteristics of the NVIDIA H100 GPU?
The Nvidia H100 offers an exceptional level of performance, based on the new Hopper architecture. This combines Tensor Core technology with the Transform engine in order to provide a greater calculation capacity and considerably accelerate the training of AI models. Nvidia offers the H100 GPU in two variants : H100 SXM and H100 NVL.
The two versions are distinguished both by their form factor and by their performance, their memory bandwidth and their connectivity. The H100 SXM is mainly designed for use in high density servers and for environments hyperscal. The H100 NVL, on the other hand, was designed for slots PCIe, which facilitates the integration of the GPU into the existing server infrastructure. The table below gives a detailed overview of the performance characteristics of the two variants of the Nvidia H100:
| Performance | NVIDIA H100 SXM | NVIDIA H100 NVL |
|---|---|---|
| FP64 | 34 tflops | 30 tflops |
| Tensor Core FP64 | 67 tflops | 60 tflops |
| FP32 | 67 tflops | 60 tflops |
| Tensor Core TF32 | 989 tflops | 835 Tflops |
| Tensor Core BFLOAT16 | 1,979 tflops | 1,671 tflops |
| Tensor Core FP16 | 1,979 tflops | 1,671 tflops |
| Tensor Core FP8 | 3,958 tflops | 3,341 tflops |
| Tensor Core int8 | 3,958 tops | 3,341 tops |
| GPU memory | 80 GB | 94 GB |
| GPU memory bandwidth | 3.35 to/s | 3.9 to/s |
| Decoder | 7 NVDEC, 7 JPEG | 7 NVDEC, 7 JPEG |
| Maximum thermal envelope power (TDP) | 700 W (configurable) | 350-400 W (configurable) |
| Multi-instance GPU (MIG) | Up to 7 mig of 10 GB each | Up to 7 mig of 12 GB each |
| Factor | SXM | Two slots with air cooling |
| Interface | NVIDIA NVLink 900 GB/S, PCIe Gen5: 120 GB/S | NVIDIA NVLink: 600 GB/S, PCIe Gen5: 128 GB/S |
| Server options | NVIDIA HGX H100 partners and NVIDIA certified systems with 4 or 8 GPU, NVIDIA DGX H100 with 8 GPU | NVIDIA certified partners and systems with 8 GPU maximum |
| NVIDIA IA Enterprise | Add-on | Included |
Note
Tflops (Tera Floating Point Operations per second) is a unit used to describe the computers' treatment speed (floating comma). A tflops corresponds to a billion of calculations per second (10¹²). The same goes for unity Tops (Tera operations per second), with the difference that it designates operations on whole numbers.
Advantages and disadvantages of the Nvidia H100
The NVIDIA H100 is among the most powerful GPUs on the market and has many technologies and advanced features. The main advantages of the H100 GPU are:
- Very large computing power : the H100 offers exceptional performance in FP8 and FP16 with its Tensor Cores, which makes it ideal for complex and intensive workloads in data such as Large Language Models (LLM). The interaction between fourth generation Tensor Cores and the Transformer engine can considerably improve the efficiency of AI operations.
- NVLink and NVSWitch : The NVIDIA H100 supports the fourth generation NVLink, which makes it possible to connect several GPUs of servers between them with a bidirectional bandwidth of 900 GB/s. Thanks to NVSWitch, it is also possible to develop the clusters in a flexible way.
- ** Multi-instance GPU (MIG): The GPU can be partitioned in maximum seven independent GPU instances, which allows you to simultaneously execute several workloads with dedicated resources. This division improves flexibility and efficiency in shared IT environments.
- Confidential Computing : thanks to the integrated security function, confidentiality and data integrity are protected throughout the work process.
- HBM3 memory and PCIe Gen5 support : With up to 94 GB of HBM3 memory and a bandwidth of up to 3.9 TB/S, the NVIDIA H100 offers one of the most efficient memories for intensive workloads in data. In combination with PCIe Gen5, it allows very fast data transfer.
However, one drawback: the high performance of the Nvidia H100 is also reflected in its price. Depending on the model, the GPU cost between 30,000 and 40,000 €. This is why H100 instances are comparatively expensive, even in cloud environments. The availability of this GPU for servers is also limited: due to the high demand, there are regular delivery delays and long waiting times.
In which areas is the NVIDIA H100 GPU best suitable?
The NVIDIA H100 GPU has been specially designed for workloads with high calculation intensity and is particularly suitable for demanding AI and HPC applications. The following overview shows the main areas of use of the H100 GPU:
- Training of large AI models : Thanks to its significant computing power, the GPU is considerably accelerating the training of complex neural networks and large linguistic models such as GPT or LLAMA.
- IA inference in real time : The H100 can perform AI models already trained at advanced speeds, which is an advantage in fields such as language processing and image recognition.
- Cloud and Data Centers : H100 is the basis of many GPU servers by providing the treatment capacity necessary for complex workloads.
- High Performance Computing (HPC): Scientific calculations and simulations benefit from high performance in FP64 of the H100 GPU.
- Generative : The Nvidia H100 is perfectly suited to the automated generation of text, images and videos with AI models. The GPU makes it possible to quickly and effectively treat large sets of data necessary for generative AI.
- Data analysis : The H100, based on Hopper architecture, helps companies in different sectors, such as logistics and finance, to make specific forecasts and predictions from large amounts of data.
GPU servers
Hardware dedicated with a powerful graphics card
Use the GPU calculation power in all flexibility to manage large amounts of data and only pay the resources used.
What are the possible alternatives to the H100 GPU?
Although the NVIDIA H100 is one of the most powerful GPUs for AI and HPC, other solutions can be envisaged depending on the planned use and budgetary constraints, for example for better profitability. Among the possible alternatives, we can cite:
- NVIDIA A100 : The previous model also offers solid performance for AI learning, inference and HPC, while being more economical.
- NVIDIA A30 : the A30 combines high performance with an affordable price.
- NVIDIA H200 : The H200 represents a slightly improved version of the NVIDIA H100 GPU, which has, for example, an even higher memory bandwidth.
- Intel Gaudi 3 : This ACCEUTER AI provides high performance for IA inference.
