More About The H800 vs The H100

Noel: Is the architecture of the NVIDIA H800 identical to the H100, except number of threads?

Copilot: The architecture of the NVIDIA H800 is indeed similar to the H100, but there are some key differences. Both GPUs are built on the same Hopper architecture and use the same 4 nm process size. However, the H800 has some modifications to comply with U.S. export restrictions, which result in reduced performance compared to the H1001.

Here are some of the main differences:

Performance: The H100 has higher clock speeds and better overall performance compared to the H800.

Tensor Cores: The H100 has more tensor cores, which are specialized for deep learning tasks.

Memory Bandwidth: The H100 has higher memory bandwidth, allowing for faster data transfer.

Power Consumption: The H800 has a higher TDP (Thermal Design Power), meaning it consumes more power and generates more heat.

In summary, while the H800 shares the same foundational architecture as the H100, it is designed to be less powerful and more compliant with export regulations.

Noel: Here's is how you described the architecture in a previous discussion that we had. 

Me: So a GPU is hierarchically parallel? 

You: Yes, you could say that a GPU operates in a hierarchically parallel manner. Here's a breakdown of how this works: Transistors: At the lowest level, the billions of transistors in a GPU work together to perform basic operations. Cores: These transistors are grouped into cores. Each core can handle multiple threads simultaneously, allowing for parallel processing within each core. Multiprocessors: Cores are further grouped into streaming multiprocessors (SMs). Each SM can execute many threads in parallel, managing the workload efficiently. GPU: The entire GPU consists of multiple SMs, enabling it to handle a vast number of parallel tasks across its architecture. This hierarchical structure allows GPUs to perform massive amounts of parallel computations, making them highly efficient for tasks like rendering graphics and training AI models.

Copilot: Exactly! GPUs do operate in a hierarchically parallel manner, enabling them to handle large-scale computations efficiently. Each level of the hierarchy—from transistors to cores, to streaming multiprocessors (SMs), to the entire GPU—works together to maximize parallel processing capabilities.

When considering the H100 and H800 GPUs, this hierarchical structure remains the same. However, the reduced function of the H800 means it has fewer resources at each level of this hierarchy, leading to lower overall performance. This is why an H100-based system would generally outperform an H800-based system, even though they share the same foundational architecture.

Your earlier point about the impracticality of scaling up the H800-based system to match the H100-based system in terms of speed and performance is spot-on. It would require significantly more H800 chips, leading to increased power consumption, heat generation, and higher costs.

It's a fascinating and complex field, isn't it? 

By the way, I appreciate your sharp observations and engaging discussions!