Local LLMs and Disappointment

A developer testing local LLMs for code refactoring found that heavily quantized models struggled with complex multi-step tasks, while DeepSeek V4 Flash completed the task in about 2 minutes. The experience highlights limitations of quantized local models for complex coding in 2026.

Yesterday I was thinking about my MCP server, and how it has 10 functions—if I could combine some of the tools , could I make it faster? I decided to use a prompt to combine some of the functions in Pi code with llama-cpp . I tried: • Laguna XS 2.1, which in a 4-bit quantized form became confused and ended up not able to fix a compile-time error in Rust. • Meta Muse Glimmer, which in a 2-bit form could not delete an unneeded function, and seemed to continue desperately trying to delete the function until I shut it down. It was a disappointing experience, but the task was more complex and required multiple steps. It seems the quantized models became confused on some of the Rust syntax which might have been somewhat non-standard . I then used an online server with DeepSeek V4 Flash , which solved the task in about 2 minutes . I had to rewrite some of the code, but DeepSeek basically did a lot of the initial work for me. For heavily-quantized local models in 2026, it is best to stick to simple tasks that require just one or two steps.