Local model performance on your hardware

generation:

375-400 tokens/s

quant:

Splash

User reports running Qwen3.6-35B-A3B (Splash build) on an M5 Max 128GB MacBook Pro in LM Studio at roughly 375-400 tok/s with a 262k context. Setup is LM Studio with the Splash build at 262k context on the 128GB machine. The speed is given only as a range, so no single figure is recorded. The post's main subject is a cloud agent running a training-and-grading program against the local model, not a benchmark of it.

generation:

375-400 tokens/s

User reports a Qwen3.6-35B-A3B Splash build in LM Studio on an M5 Max 128GB MacBook Pro at roughly 375-400 tok/s, used to generate and cold-solve 800 math problems. A Qwopus 122B MLX build at about 38 tok/s acts as judge for disputed items; the cloud agent Muse orchestrates over Tailscale. Of 800 problems, 731 verified clean, 62 had corrected answer keys, 7 were excluded, and 100 were held out; the solve-and-adjudication passes logged 4.1M+ completion tokens. The user notes the 122B judge went 1-for-2 on the hardest disputes.

generation:

26.0 tokens/s

prompt processing (prefill):

190.0 tokens/s

quant:

IQ2XXS (GGUF)

kv:

F16

flash attention:

on

User reports DeepSeek V4 Flash at ~26 tok/s generation and ~190 tok/s prompt processing on a MacBook Pro M5 Max with 128 GB unified memory. Setup is LM Studio with antirez's deepseek_v4 llama.cpp fork sideloaded, IQ2XXS GGUF weights, F16 KV cache, and the full 1048576 token context. The model is a 256-expert MoE with ~80 GB resident. The figures are approximate and the run is a workaround: upstream llama.cpp does not yet support the deepseek_v4 architecture and LM Studio's MLX runtime rejects V4 GGUFs. Long prompts beyond 16k tokens are flushed in 16k chunks due to the n_batch cap.