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Comment by Const-me | Hacker News Reader

> a lot of new models require the higher-end 80GB A100 variants

For training, yeah. But at least in some cases, inference can be optimized to use less memory.

For their large Whisper model, OpenAI says they need 10GB of VRAM: https://github.com/openai/whisper#available-models-and-langu...

My DirectCompute port of that uses slightly over 4GB of VRAM, see the last column in that table: https://github.com/Const-me/Whisper/blob/master/SampleClips/... I haven’t actually optimized memory usage I only did the DirectCompute port, the memory savings were achieved by Georgi Gerganov in whisper.cpp project, which I ported to Windows.

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apparently GPT-J needs ~25GB RAM min (for inference only), i.e. a 32GB card ...and GPT-3 is an order of magnitude bigger again

https://nlpcloud.com/gpt-3-open-source-alternatives-gpt-j-gp...

though TBH just making cards with more RAM to fit these kind of models seems easier than being at absolute cutting edge of performance

I also wonder how this metric translates to something like Apple Silicon 'unified memory'... latest MBP have 32GB RAM, I wonder if anyone managed to run GPT-J there yet?

There's a float16 version of GPT-J rewritten in C++ here that apparently fits into 16GB...

https://github.com/ggerganov/ggml/tree/master/examples/gpt-j

it's CPU-only and runs inference at "about ~6 words per second" on an M1 MBP

That's for the max-size model (6B parameters). GPT-Neo has pruned models 125m and 1.3b that fit into 1.5gb and 6gb of memory respectively.

These models are obviously nerfed, but you can run them on budget ARM hardware and get legible, paragraph-length responses in less than 10 seconds.

There's a 20B model now

The 20B model was the original I believe, and the other models were all pruned from it.

This is incorrect. Thought would be interesting. Pruning usually removes some parameters from the model (setting certain ones to 0 for instance). The smaller models all have less layers and therefore weren’t pruned but were smaller to begin with.

They have a link to the bottom for something similar, but with GPT-2. The Hugging Face docs have hints for using FP16, and avoiding double memory requirements for model and checkpoint data.

You can also use int8 quantization which comes for free with huggingface and isn’t a huge cost to accuracy. Which cuts the memory usage almost in half again. (almost in half because some layers might stay 32 bit if they make a tiny part of the model)

I am running GPT-J-6B inference on 13GB of VRAM. FP16 is a must at this scale. I've also loaded GPT-NeoX-20B using int8 quantization onto a single RTX 3090 before (24GB VRAM) however inference latency was quite high.

Actually, the MacBook Pro with M2 Max CPU can be configured with 96GB of ram. The Mac Studio with M1 Ultra can have up to 128GB of ram.

Ah great, I hadn't seen that... seems like that should be plenty for some of these models!

I've been running the stock implementation that OpenAI put out, I didn't know people had made so many improvements!

Thank you for your work.

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