In principal you can run it on just about any hardware with enough storage space. It's just a question of how fast it will run. This readme has some benchmarks with a similar set of models (and the code has support for even swapping data out to disk if needed): https://github.com/FMInference/FlexGen
As the models proliferate, I guess we'll be finding out soon. The torrent has been going pretty slow for me for the past couple hours, but it looks like there are a couple seeders, so eventually it'll hit that inflection point where there are enough seeders to give all the leechers full speed downloads.
Looking forward to the YouTube videos of random tinkerers seeing what sort of performance they can squeeze out of cheaper hardware.
The 30B is 64.8GB and the A40s have 48GB NVRAM ea - so does this mean you got it working on one GPU with an NVLink to a 2nd, or is it really running on all 4 A40s?
Is there a sub/forum/discord where folks talk about the nitty-gritty?
> so does this mean you got it working on one GPU with an NVLink to a 2nd, or is it really running on all 4 A40s?
it's sharded across all 4 GPUs (as per the readme here: https://github.com/facebookresearch/llama). I'd wait a few weeks to a month for people to settle on a solution for running the model, people are just going to be throwing pytorch code at the wall and seeing what sticks right now.
> people are just going to be throwing pytorch code at the wall
The pytorch 2.0 nightly has a number of performance enhancements as well as ways to reduce the memory footprint needed.
But also, looking at the README, it appears that model alone needs 2x the model size, eg 65B needs 130GB NVRAM, PLUS the decoding cache which stores 2 * 2 * n_layers * max_batch_size * max_seq_len * n_heads * head_dim bytes = 17GB for the 7B model (not sure if it needs to increase for the 65B model), but maybe a total of 147GB total NVRAM for the 65B model.
That should fit on 4 Nvidia A40s. Did you get memory errors, or you haven't tried yet?
So since making that comment I managed to get 65B running on 1 x A100 80GB using 8-bit quantization. Though I did need ~130GB of regular RAM on top of it.
It seems to be about as good as gpt3-davinci. I've had it generate React components and write crappy poetry about arbitrary topics. Though as expected, it's not very good at instructional prompts since it's not tuned for instruction.
People are also working on adding extra samplers to FB's inference code, I think a repetition penalty sampler will significantly improve quality.
The 7B model is also fun to play with, I've had it generate Youtube transcriptions for fictional videos and it's generally on-topic.
