Comment by gwerbin

I tried this on my work M4 with 48 GB of memory and it's not as useful as I had hoped.

If you set a large context window you end up using all your memory. Prefill is slow. And the 10-20B models are somewhat poor at understanding intent and tend to think in circles, so if you set a small thinking budget to control context size the model ends up answering based on some half formed thoughts.

Or something like batch document summarization or image classification or whatever yeah it's great. But for "agentic" and coding workflows I think you would need a completely different harness setup and prompting style compared to what we are used to from the big proprietary models. I certainly have not yet figured out what that is.