Deep Learning and AI

Which NVIDIA H100 is Best for Training LLMs and NLP Models

June 8, 2023 • 6 min read


What do You need for Training LLMs?

Large Language Models (LLMs) are a type of Language Model consisting of a neural network of parameters trained on massive amounts of unlabeled textual data. They need extremely high-performance GPUs responsible for calculating the weights in the neural network.

As we already know, GPUs are perfect for training AI models due to their high degree of parallelism for matrix operations commonly found in AI tasks like Natural Language Processing (NLP). With an application and model like ChatGPT, the billions of parameters and the need to deliver accurate responses in real-time necessitates the best of the best. No average GPUs, even the touted powerhouse RTX 4090, could rein in an AI model.

What GPU to Get?

NVIDIA’s data center GPUs provide the best performance to deliver the best LLM/NLP model. Making it possible for our computers to interact with the real world is now becoming a tangible possibility. Large Language Models (LLMs) find applications in diverse sectors, including personalized chatbots, automated customer service, sentiment analysis, content creation, and even coding.

Industry leaders and even startups choose NVIDIA and their data center GPUs for their integration with CUDA and deep learning frameworks. The standout GPUs from NVIDIA are the A100 and its newly released brother, the NVIDIA H100. The NVIDIA H100 comes in 2 form factors, PCIe and SXM5. NVIDIA also announced the GH200 CPU-GPU superchip as well something we have never seen. So which NVIDIA H100 is the best one to get?


The NVIDIA H100 comes in the classic PCIe form factor. They can be linked together with 3 NVLink bridges for a 600GB/s bidirectional bandwidth. The NVIDIA H100 is PCIe 5.0 and is an extremely capable GPU that can be deployed at ease by slotting into existing infrastructure. It can also be easily upgradable for the next generation of accelerators.

Each NVIDIA H100 comes with 80GBs of HBM3 memory. HBM3, or high bandwidth memory, is perfect for LLMs due to the high data transfer speeds. More speed between GPUs means more performance for large datasets. When more data is involved, the more these GPUs need to talk to each other, and that's where NVIDIA’s form factor offerings come into play.


The SXM architecture is the idea of socketing the GPU in a proprietary slot for connecting GPUs on a unified system board. The HGX system board sockets 4 or 8 NVIDIA H100 SXM5 GPUs on a board for high GPU-to-GPU interconnect. The NVLink switch system on the HGX system board connects all the GPUs to have 900GB/s bandwidth between any two GPUs as opposed to the PCIe where only pairs of GPUs can take advantage of the NVLink.

More GPU interconnect means that fewer data has to transfer over the bottlenecked PCIe lanes that cap at 128GB/s bidirectional. With 900GB/s bidirectional bandwidth is leagues better for these GPUs to communicate effectively when training the largest and most complex LLMs.

NVIDIA’s DGX H100 is designed to be scaled even further in the data center. Multiple DGX can be further NVLinked via the NVLink Switch System. That is one of the biggest reasons why data centers and startups gravitate towards NVIDIA DGX. With more GPUs interconnected, the faster the training of the AI models, and the faster the real-time inferencing.

If the degree of scalability is not a big priority and customizability is a plus, various manufacturers and systems integrators (like us here at SabrePC) also have customizable servers built using the standalone HGX system board. Pick your own CPUs, slot your own networking accelerators, and take advantage of the cool form factors, additional hot-swap drive bays and more.

Explore our selection of platforms supporting NVIDIA H100 PCIe and NVIDIA HGX H100. Our systems are built and configured to your specification and thoroughly validated to bring your computing to the next level.

NVIDIA GH200 Grace Hopper Superchip

Recognizing the increasing demand for training LLMs and other highly data-reliant AI models drove NVIDIA to the innovative development of its Grace CPU. Built using ARM cores make the Grace CPU an exceptionally performant processor while being power efficient.

But what makes the CPU stand out is the ability to be linked using NVIDIA’s NVLink C2C interconnect. NVLink 2 Grace CPUs for a dual Grace CPU. Or NVLink a Grace CPU and an H100 for the newly named Grace Hopper Superchip GH200. By linking the CPU and the GPU with the faster NVLink chip-to-chip interconnect, the bandwidth between the CPU and GPU is 7x faster than traditional PCIe at 900GB/s. Memory between the CPU and GPU is shared so GPU can access the 480GB of LPDDR5x CPU memory while the CPU can access the 96GB of HBM3 GPU memory.

Grace CPU is an ARM CPU, designed for single-threaded performance, perfect for application deployments like Generative AI where each instance and prompt is executed and inferences on a single CPU. Grace Hopper is a 1:1 CPU GPU ratio combo meaning cloud applications, inferencing, and virtualization are the main focus for this type of hardware. The availability of Grace Hopper is expected to be by the end of the year.

What NVIDIA H100 GPU is best for AI Models: LLMs, NLP, and Generative AI

  • NVIDIA H100 PCIe is good for quick and easy integration and implementation in existing data centers.
  • NVIDIA HGX H100 is good for high-performance training and is designed for customizability to match your data center.
  • NVIDIA DGX H100 similar to HGX is noncustomizable but can be scaled with multiple DGX on an NVLink-based infrastructure, perfect for those that want a unified data center of computing.
  • NVIDIA GH200 suits those working with real-time data inferencing and training for multiple instances and deployment.

In the end, NVIDIA H100 you pick will ultimately depend on your infrastructure. Weigh your pros and cons, talk to a specialist in data center computing, and understand your workload for the best performance for the cost. Don't go overkill on the computing as you don't want to spend more than you need leaving multi-thousand dollar GPUs with nothing to do.

At SabrePC we can deliver the best knowledge and best configuration to your budget and workload.
Contact us today for more information.


deep learning and ai



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