3 ms·
Gemma 30B with 256K context runs at 20 tok/sec on my M3 Max with 128GB RAM so I think there’s something wrong with your setup. This should run at ~30-40 toks. M
by aetherspawn 15d ago
Gemma 30B with 256K context runs at 20 tok/sec on my M3 Max with 128GB RAM so I think there’s something wrong with your setup. This should run at ~30-40 toks. Maybe your inference engine is not optimised for Mac.
- huseyinkeles 15d agoI just used their `Bonsai-demo` repo like this; `cd ~/Code/Bonsai-demo && BONSAI_CTX=65536 ./scripts/start_llama_server.sh` then used it in a very minimalistic pi with a very small system prompt. Didn't spend much time to try to optimize it tbh, but my issue was not the speed. it just could not make a decision on how to implement the task, kept going on an on.
- yearolinuxdsktp 14d agoMaybe you have to set reasoning effort to low. 3.8 27B on x-high (default) reasons forever on anything complex. I asked it to write down the answer plan so far leaving open questions as open and it wrote the plan twice in reasoning (and more times partially) while it dilly-dallied about open questions before realizing “ok the user just asked to leave questions open.” That was a Q6o quant with unquantized KV cache.
- aetherspawn 13d agoYes, I use LM Studio with MLX support, which is specifically faster on M series Macs. I am not sure if llama is the same, but I guess what I’m saying is if you want the performance to be good on M series you have to use models packaged in the right format.
- piyh 15d ago30B is useless on 24 gigs of ram as there's ~4 gigs of ram left for everything else even with unsloth quants
- BoredomIsFun 13d ago31B