What is everyone doing with all of these GPUs?

Question

I know accelerator demand is blowing up. However, there are only a few players training foundation models. What are the core use cases everyone else has for all of these accelerators? Fine tuning, smaller transformer models, general growth in deep learning?

lafeoooooo · Accepted Answer

Different scenarios have varying demands for GPU types. For tasks like model inference or basic operations, a CPU or even on-device solutions (mobile, web) might suffice.
When a GPU is necessary, common choices include T4, 3090, P10, V100, etc., selected based on factors like price, required computing power, and memory capacity.
Model training also has diverse needs based on the specific task. For basic, general-purpose vision tasks, 1 to 50 cards like the 3090 might be enough. However, cutting-edge areas like visual generation and LLMs often require A100s or A800s, scaling from 1 to even thousands of cards.

talldayo · Answer

Inference. 99% of the customers that aren't buying GPUs to train on are either using it for inference or putting it in a datacenter where inference is the intended use-case.

the__alchemist · Answer

I'm playing UE5 games, and doing some computational chem with CUDA.