阿里开源 Qwen3.8-27B 本地实测：性能很强，但 Agent 适配仍待补课
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Merged summary
TL;DR - Alibaba’s open-source Qwen3.8-27B delivers strong local inference and high-quality agent outputs, but its excessive reasoning, context reuse, and tool calls make complex agent tasks costly and slow. The tests show that practical agent performance depends as much on efficient planning and execution control as on model capability.
- Qwen completed all nine agent tasks at full quality, versus 14/18 for the locally deployed DeepSeek-V4-Flash-0731-Q4 baseline.
- That quality required 13.99 million tokens and about 6 hours 17 minutes—roughly 14.6× the tokens and 16.8× the runtime of DeepSeek.
- vLLM and SGLang exceeded 40 tokens/s on A100 GPUs, while Llama.cpp reached 23.5 tokens/s but offered a lower deployment threshold.
- In the tested text tasks, 3-bit to 6-bit quantization preserved full scores; complex agent use still requires strict limits on tokens, step scope, context size, and stopping conditions.
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阿里开源 Qwen3.8-27B 本地实测：性能很强，但 Agent 适配仍待补课
TL;DR - Alibaba’s open-source Qwen3.8-27B delivers strong local inference and high-quality agent outputs, but its excessive reasoning, context reuse, and tool calls make complex agent tasks costly and slow. The tests show that practical agent performance depends as much on efficient planning and execution control as on model capability.
- Qwen completed all nine agent tasks at full quality, versus 14/18 for the locally deployed DeepSeek-V4-Flash-0731-Q4 baseline.
- That quality required 13.99 million tokens and about 6 hours 17 minutes—roughly 14.6× the tokens and 16.8× the runtime of DeepSeek.
- vLLM and SGLang exceeded 40 tokens/s on A100 GPUs, while Llama.cpp reached 23.5 tokens/s but offered a lower deployment threshold.
- In the tested text tasks, 3-bit to 6-bit quantization preserved full scores; complex agent use still requires strict limits on tokens, step scope, context size, and stopping conditions.