Machine Learning

1 readers

1 users here now

Community Rules:

Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.

founded 11 months ago

MODERATORS

communick@academy.garden

[D] How large an LLM can I train from scratch on a single A100 GPU with 80Gb memory? (alien.top)

submitted 10 months ago by eeeehhh@alien.top to c/machinelearning@academy.garden

4 comments fedilink hide all child comments

I have access to a single 80Gb A100 GPU and would like to train an LLM with GPT-like architecture from scratch. Does anyone know how to calculate the maximum model size.

you are viewing a single comment's thread
view the rest of the comments

[–] AI-Guru011010@alien.top 1 points 10 months ago

With bfloat16 and flash attention you can fully pretrain a 200M parameter encoder-decoder model on millions of data samples in as little as a couple of weeks. You’re going to have to really focus on optimizing your workflow so that you’re limited by gpu utilization, you don’t want your gpu sitting around waiting for data or anything. I’ve also been able to train models with >650m parameters and a sequence length of 4096 on a single A100 using huggingface accelerate, albeit much more slowly