Deepseek referred here seems to be v3, not r1. While the linked article didn’t seem to have info on parameter size, fact that they state it is sparse MoE architecture should suggest it is capable to run pretty quick (compared to other models of similar parameter space), so that’s cool.
Depending on what you want to do with it, and what your expectations are; the smaller distilled versions could work on CPU, but most likely will need extra help on top, just like other similar sized models.
This being a reasoning model, you might get a more well thought out results out of it, but at the end of the day, smaller parameter space (easiest to think as ‘less vocabulary’), smaller capabilities.
If you just want something to very quickly chat back and forth with on a CPU, try IBM’s granite3.1-moe:3b, which is very fast even on a modern CPU, but doesn’t really excel in complex problems without additional support (ie: RAG or tool use).
Yep! Give granite a try. I think that would be perfect for this use case both in terms of able to answer your queries and doing them quickly, without a GPU by just using modern CPU. I was getting above 30 tokens per second on my 10th gen i5, which kind of blew my mind.
Thinking models like r1 will be better at things like troubleshooting a faulty furnace, or user problems, so there’s benefits in pushing those envelopes. However, if all you need is to give basic instructions, have it infer your intent, and finally perform the desired tasks, then smaller mixture of experts models should be passable even without a GPU.