The Best GPU For AI & HPC? Should You Buy NVIDIA H200?

NVIDIA H200 Specifications

NVIDIA H200 comes in 2 options — HGX and PCIe.

• NVIDIA HGX H200: The NVIDIA HGX H200 is the data center SXM5 variant featuring 4 or 8 interconnected GPUs via NVLink for increased GPU-to-GPU communication essential for workloads that require multiple GPUs to work together.
• NVIDIA H200 NVL: The NVIDIA H200 NVL is a more mainstream option in the PCIe form factor that uses NVLink bridges for interconnecting GPUs. However, the H200 NVL is likely more suitable for workloads that parallelize the workload across multiple GPUs.

The main difference between HGX and H200 NVL is scalability. HGX H200 can be further scaled with multiple HGX systems for an even more interconnected GPU deployment. The fast NVLink interconnect and NVLink Switch system enables these GPUs to communicate at lightning-fast 4.8TB/s speeds.

NVIDIA H200 Use Cases

NVIDIA H200 in both HGX and NVL options is suitable for training AI, real-time data analytics, engineering simulation, and HPC.

• Training Foundational Models & Complex AI
• Real-Time Data Analytics
  • Weather Modeling, Prediction Algorithms
• Engineering Simulation
  • FEA, CFD, Molecular Dynamics

We will go over what kinds of deployments are best suited to fully utilize a deployment equipped with NVIDIA H200 and highlight which H200 (HGX vs PCIe) would be best.

Training Foundational Models & Complex AI

NVIDIA’s endeavors in developing the Tensor Core GPUs like the A100, H100, and now H200 have all been hyper-focused on accelerating AI training performance. As AI models become larger and larger, the need for interconnected GPUs led to the further development of NVLink technology.

Training foundational AI models for LLMs and Generative AI necessitates huge amounts of data and thus a huge GPU memory capacity to reduce callbacks to solid-state storage. If an AI model can perform its calculations on the neural network straight off GPU memory, you can limit data fetch bottlenecking.

Furthermore, training novel AI models for your workloads like fraud detection, recommendation systems, and other real-time data analysis can also benefit from the added performance of NVIDIA H200. But in this case, storing the entire model in GPU memory is not quite as essential. The H200 NVL PCIe version will be sufficient.

However, this mainly applies to only the training and powering of these foundational AI models. Once the model is trained, inferencing is significantly less compute-intensive. But that doesn’t mean H200 is not needed anymore. Companies that host their AI as an API call will require a multi-instance where each prompt can be tackled in parallel.