Running Qwen 3.6 35B-A3B at a Q4 quant with a full 262K context window on a NUC12's 16GB Intel Arc A770M, this person found that all the model's coding-relevant experts fit in VRAM alongside the entire context, which keeps it fast for the hardware. They plan changes out first in a cloud chat with Claude, ChatGPT, or Qwen, then hand a concrete prompt to the local setup — running llama.cpp on the NUC with the Pi code harness on their laptop — and let it open pull requests at roughly 10–15 tokens/sec in the background while they do other things. They've pushed it through real, if modest, changes on their homelab codebase, hitting around 60% context usage with good results.
VERIFIABLE SOURCE
View comment Reported anonymously by an r/LocalLLM contributor · score 2