Ask HN: What is everyone doing with all of these GPUs?

[formercoder]

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]

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]

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.

[kkielhofner]

Absolutely.

For some reason inference seems to be overlooked. A lot of “ink” has been spilled over GPUs for training tasks but at the end of the day if you can’t do inference you can’t serve users and you can’t make money.

[formercoder]

Think the massive increase in demand is due to mass inference of open source LLMs? Or is the transformer architecture driving mass inference of other models too?

[the__alchemist]

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