> The the question is if a llm will run with usable performance at that scale?
This is the big question to have answered. Many people claim Apple can now reliably be used as a ML workstation, but from the numbers I've seen from benchmarks, the models may fit in memory, but the performance for tok/sec is so slow to not feel worth it, compared to running it on NVIDIA hardware.
Although it be expensive as hell to get 512GB of VRAM with NVIDIA today, maybe moves like this from Apple could push down the prices at least a little bit.
It is much slower than nVidia, but for a lot of personal-use LLM scenarios, it's very workable. And it doesn't need to be anywhere near as fast considering it's really the only viable (affordable) option for private, local inference, besides building a server like this, which is no faster: https://news.ycombinator.com/item?id=42897205
Could you maybe share a lightweight benchmark where you share the exact model (+ quantization if you're using that) + runtime + used settings and how much tokens/second you're getting? Or just like a log of the entire run with the stats, if you're using something like llama.cpp, LMDesktop or ollama?
Also, would be neat if you could say what AI services you were subscribed to, there is a huge difference between paid Claude subscription and the OpenAI Pro subscription for example, both in terms of cost and the quality of responses.
Hm, the AI services over 5 years cost half of m4 max minimal configuration which can barely run severely lobotomized LLaMA 70B. And they provide significantly better models.
Sure, with something like Kagi you even get many models to choose from for a relatively low price, but not everybody likes to send over their codebase and documents to OpenAI.
Do we know if is it slower because of hardware is not as well suited for the task or is it mostly a software issue -- the code hasn't been optimized to run on Apple Silicon?
AFAICT the neural engine has accelerators for CNNs and integer math, but not the exact tensor operations in popular LLM transformer architectures that are well-supported in GPUs.
The neural engine is perfectly capable of accelerating matmults. It's just that autoregressive decoding in single batch LLM inference is memory bandwidth constrained, so there are no performance benefits to using the ANE for LLM inference (although, there's a huge power efficiency benefit). And the only way to use the neural engine is via CoreML. Using the GPU with MLX or MPS is often easier.
This is the big question to have answered. Many people claim Apple can now reliably be used as a ML workstation, but from the numbers I've seen from benchmarks, the models may fit in memory, but the performance for tok/sec is so slow to not feel worth it, compared to running it on NVIDIA hardware.
Although it be expensive as hell to get 512GB of VRAM with NVIDIA today, maybe moves like this from Apple could push down the prices at least a little bit.