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Comment by BossingAround | Hacker News Reader

An off-topic question, are Apple's M-series chips any good at current AI/ML work? How does it compare with dedicated GPUs?

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I have an M3 chip in my laptop, it has more memory than my 4090 but it's still way slower when inferencing. So as long as the model fits in memory, Nvidia GPUs are going to be way faster just because they have more/faster compute cores.

Of course, if the model fits in memory on your M chip and doesn't in your Nvidia chip, the M chip wins by default. However, I would say, if you load a 70B model in your M chip, while it WILL work, the tokens/sec will be slow as fuck... so it kinda doesn't matter anyway.

The latest Nvidia drivers offer an option to start using system memory when VRAM is insufficient. It certainly slows things down, but it does work. It's not perfect in my experience, but it is an alternative for large models.

Sounds great! Do you have source maybe a wiki?

This might be the best place to start:

https://nvidia.custhelp.com/app/answers/detail/a_id/5490/~/s...

How does the inference for LLMs impact battery life? For SD it can be 5% battery per image at times.

Mid-tier gaming GPU performance, but (potentially) access to gobs of memory (if running on a host with gobs of memory) owing to the unified memory design. For certain use cases which require loading huge datasets but don't necessarily require massive compute (i.e. inference on large models) they can be cost-competitive relative to something like an H100.

Compared to a GPU like a 4090 with equal vram it probably won't fare well as others point out, however it far outperforms any CPU. On an M1 Ultra MacBook Pro I was seeing like 40 tokens/second with llama3:7B vs 9 tokens/second on various Intel servers/desktops with sufficient ram.

I've only tried it on my M1, running Llama-3 via Ollama. It works, but it's slow to the point where it's not really usable. Maybe there are smaller models you can run that will perform better.

What size model did you try and how much memory does your M1 have? See my other comment, my experience has been that llama3 was very fast on an M1.

I was just running: ollama run llama3, So that would be 8B parameters, on a 8GB M1 Air.

Maybe it's just my expectations, but it seems rather slow to process queries. Depending on the prompt somewhere between the 10 - 40 tokens per second, but that very much depends on the prompt.

My complaint is the time between the prompt is entered and output starts.

If apple offers these in a data center that’s open access it’s game over for NVDA

Based on which fantasy premise?

The premise where these are readily available for mass purchase, have a hardware and software stack that already works reliably, and have a lower energy footprint than other offerings

and somewhat competitive on cost, but that wont be the main selling point, just availability

For the purposes we're discussing, they're nowhere near competitive with Nvidia.

lower energy but at least they're slow? You need to add up to the same performance level and then consider the cost and running cost. I bet it's not close on that.

Availability maybe, but as you've noted - zero availability for data center environments. Those volumes would also then fall onto TSMC/Samsung/Whatever where Nvidia is stuck as well.

I’m aware Apple is also beholden to TSMC’s capacity too

sadly

They aren't even on the same league, computing-wise.

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