Qwen 3.8-27B Q6 Shows Strong Performance in Local Agentic Coding
A local LLM user reported successful real-world performance using the quantized Qwen3.8-27B Q6 model for agentic coding. During a continuous 20-hour session, the model maintained a processing speed of 60 to 63 tokens per second. This demonstrates that consumer-grade hardware setups, like combining an RTX 3090 and an RTX 3060, can effectively run highly capable quantized models for long-duration coding tasks. It highlights the viability of running advanced AI coding agents locally without relying on expensive cloud APIs. The setup utilized a dual-GPU configuration consisting of an RTX 3090 and an RTX 3060 to distribute the workload. The model used was a Q6 quantization of the 27-billion-parameter Qwen model, balancing memory footprint and model intelligence.
## BACKGROUND
Agentic coding refers to using AI agents to perform multi-step software development tasks, such as code generation, debugging, and testing, with minimal human intervention. Quantization, such as the Q6 format, is a technique that shrinks Large Language Models (LLMs) by reducing the precision of their weights, allowing them to run on consumer hardware with minimal loss in accuracy.