Qwen3.8-27B Q6 Sustains 60+ Tokens/s
💡See whether a quantized 27B model can sustain practical local agentic coding for 20 hours.
⚡ 30-Second TL;DR
What Changed
The model completed nearly 20 hours of continuous agentic coding work.
Why It Matters
If reproducible, this suggests that a 27B-class quantized model can support long-running local coding agents at a practical interactive speed. However, coding quality, context handling, and workload characteristics still need independent verification.
What To Do Next
Run a reproducible 2-hour coding-agent test with Qwen3.8-27B Q6 in llama.cpp, recording tokens per second, context usage, task success rate, and GPU memory.
Key Points
- •The model completed nearly 20 hours of continuous agentic coding work.
- •Reported throughput remained around 60–63 tokens per second.
- •The test used a system combining an RTX 3090 and an RTX 3060.
- •The result is a community field report rather than a controlled benchmark.
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Original source: Reddit r/LocalLLaMA ↗
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