User Reports Issues Using Qwen 3.8 27B for Agentic Coding in VSCode
A developer shared their struggle using a quantized version (Q6_K) of the Qwen 3.8 27B model for agentic coding tasks within VSCode. Despite using powerful hardware (2x RTX 3090 Ti) and tools like Cline and MCP, the model suffered from looping issues and failed to complete tasks correctly compared to DeepSeek and Claude. This highlight shows that even with capable local hardware and high-quality quantized models, running complex agentic workflows locally remains challenging compared to cloud-based APIs. It underscores the ongoing gap in reasoning and tool-use reliability between local open-weight models and proprietary solutions like Claude. The user ran the Q6_K quantization of the model via LM Studio on Windows 11 with a 50k token context window, fully offloaded to dual GPUs. Despite adjusting prompt templates to reduce reasoning effort, the model repeatedly consumed excessive tokens and attempted to fix non-existent issues.
## BACKGROUND
Agentic coding involves AI models acting as agents that can autonomously use tools, read files, and write code via extensions like Cline and the Model Context Protocol (MCP). Quantization (like Q6_K) is a technique used to compress large language models so they can fit into local GPU memory, though it can sometimes degrade reasoning performance.