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Coding · 2026-09-11

Background coding at 10-15 tok/s on an Intel Arc laptop GPU

A NUC12 with a 16GB Intel Arc A770M handles a 262K-context Qwen build entirely in VRAM, coding in the background while the owner does something else.

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 the local model a concrete prompt 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.

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SETUP HISTORY

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CURRENT2026-09-11Background coding at 10-15 tok/s on an Intel Arc laptop GPU

A NUC12 with a 16GB Intel Arc A770M handles a 262K-context Qwen build entirely in VRAM, coding in the background while the owner does something else.

Coding1 machineQwen 3.6 35B-A3Bllama.cpp
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DescriptionPreviousRunning 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.This snapshotRunning 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 the local model a concrete prompt 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.

This is the currently published snapshot.

REVISION 12026-09-11Background coding at 10-15 tok/s on an Intel Arc laptop GPU

A NUC12 with a 16GB Intel Arc A770M handles a 262K-context Qwen build entirely in VRAM, coding in the background while the owner does something else.

Coding1 machineQwen 3.6 35B-A3Bllama.cpp

Archived published snapshot — kept for context and comparison.

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