DS4 can process 460 prompt tokens per second. Not stellar but not so slow. On M3 max. See the benchmarks on readme.
replyCan you ELI5 why this is so slow for local inference but so fast for using hosted models?
replyAnd unless I'm mistaken, the repo is about running it with 2bit quantization.
replyThis is probably far from the raw intelligence provided by cloud providers.
Still, this shines more light on local LLMs for agentic workflows.
It runs both q2 and original (4 bit routed experts). At the same speed more or less. The q2 quants are not what you could expect: it works extremely well for a few reasons. For the full model you need a Mac with 256GB.
replyOut of curiosity, do you have any theories of why it works so well at such aggressive quantization levels?
replyIt's a mix of extreme sparsity but with the routed expert doing a non trivial amount of work (and it is q8), and projections and routing not being quantized as well. Also the fact it's a QAT model must have a role I guess, and I quantized routed experts out layers with Q2 instead of IQ2_XXS to retain quality.
replyWhy is this the case?
replyAre there any architectures that don't rely on feeding the entire history back into the chat?
Recurrent LLMs?