Comment by terhechte
14 days ago
To add, they say about the 400B "Maverick" model:
> while achieving comparable results to the new DeepSeek v3 on reasoning and coding
If that's true, it will certainly be interesting for some to load up this model on a private M3 Studio 512GB. Response time will be fast enough for interaction in Roo Code or Cline. Prompt processing is a bit slower but could be manageable depending on how much code context is given to the model.
The upside being that it can be used on codebases without having to share any code with a LLM provider.
Small point of order: bit slower might not set expectations accurately. You noted in a previous post in the same thread[^1] that we'd expect about a 1 minute per 10K tokens(!) prompt processing time with the smaller model. I agree, and contribute to llama.cpp. If anything, that is quite generous.
[^1] https://news.ycombinator.com/item?id=43595888
I don't think the time grows linearly. The more context the slower (at least in my experience because the system has to throttle). I just tried 2k tokens in the same model that I used for the 120k test some weeks ago and processing took 12 sec to first token (qwen 2.5 32b q8).
Hmmm, I might be rounding off wrong? Or reading it wrong?
IIUC the data we have:
2K tokens / 12 seconds = 166 tokens/s prefill
120K tokens / (10 minutes == 600 seconds) = 200 token/s prefill
> The more context the slower
It seems the other way around?
120k : 2k = 600s : 10s