# Local LLMs and Disappointment

> Source: <https://www.dotnetperls.com/2026_8_18_local-llms-disappointment>
> Published: 2026-08-18 07:00:00+00:00

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.