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The coolness of local LLMs is THE only reason I am sadly eyeing upgrading from M1 64GB to M4/5 128+GB.
by noman-land 2y ago
The coolness of local LLMs is THE only reason I am sadly eyeing upgrading from M1 64GB to M4/5 128+GB.
- Terretta 2y agoCompare performance on various Macs here as it gets updated: https://github.com/ggerganov/llama.cpp/discussions/4167 https://github.com/ggerganov/llama.cpp/discussions/4167 OMM, Llama 3.3 70B runs at ~7 text generation tokens per second on Macbook Pro Max 128GB, while generating GPT-4 feeling text with more in depth responses and fewer bullets. Llama 3.3 70B also doesn't fight the system prompt, it leans in. Consider e.g. LM Studio (0.3.5 or newer) for a Metal (MLX) centered UI, include MLX in your search term when downloading models. Also, do not scrimp on the storage. At 60GB - 100GB per model, it takes a day of experimentation to use 2.5TB of storage in your model cache. And remember to exclude that path from your TimeMachine backups.
- noman-land 2y agoThank you for all the tips! I'd probably go 128GB 8TB because of masochism. Curious, what makes so many of the M4s in the red currently.
- vessenes 2y agoIt's all memory bandwidth related -- what's slow is loading these models into memory, basically. The last die from Apple with all the channels was the M2 Ultra, and I bet that's what tops those leader boards. M4 has not had a Max or an Ultra release yet; when it does (and it seems likely it will), those will be the ones to get.
- ant6n 2y agoWhat if you have a Macbook Air with 16GB (the bechmarks dont seem to show memory).
- simonw 2y agoYou could definitely run an 8B model on that, and some of those are getting very capable now. The problem is that often you can't run anything else. I've had trouble running larger models in 64GB when I've had a bunch of Firefox and VS Code tabs open at the same time.
- xdavidliu 2y agoI thought VSCode was supposed to be lightweight, though I suppose with extensions it can add up
- chris_st 2y agoI have a M2 Air with 24GB, and have successfully run some 12B models such as mistral-nemo. Had other stuff going as well, but it's best to give it as much of the machine as possible.
- gcanyon 2y agoI recently upgraded to exactly this machine for exactly this reason, but I haven't taken the leap and installed anything yet. What's your favorite model to run on it?
- evilduck 2y ago8B models with larger contexts, or even 9-14B parameter models quantized. Qwen2.5 Coder 14B at a 4 bit quantization could run but you will need to be diligent about what else you have in memory at the same time.
- stkdump 2y agoI bought an old used desktop computer, a used 3090, and upgraded the power supply, all for around 900€. Didn't assemble it all yet. But it will be able to comfortably run 30B parameter models with 30-40 T/s. The M4 Max can do ~10 T/s, which is not great once you really want to rely on it for your productivity. Yes, it is not "local" as I will have to use the internet when not at home. But it will also not drain the battery very quickly when using it, which I suspect would happen to a Macbook Pro running such models. Also 70B models are out of reach of my setup, but I think they are painfully slow on Mac hardware.
- jazzyjackson 2y agoI'm returning my 96GB m2 max. It can run unquantized llama 3.3 70B but tokens per second is slow as molasses and still I couldn't find any use for it, just kept going back to perplexity when I actually needed to find an answer to something.
- Tepix 2y agoInteresting. You're using the FP8 version i'm guessing? How many tokens/s are you using and which software? MLX?
- alecco 2y agoI'm waiting for next gen hardware. All the companies are aiming for AI acceleration.
- kleiba 2y agoSorry, I'm not up to date, but can you run GPTs locally or only vanilla LLMs?