# Bonsai 1.7B for MCP Server

> Source: <https://www.dotnetperls.com/2026_10_14_bonsai-1-7b-mcp-server>
> Published: 2026-10-14 07:00:00+00:00

Now that I have a **custom MCP server** running locally, I need an **LLM** (running in `llama-cpp`) to control it. I have found most any model will work; the main challenge is finding the fastest, smallest, and most accurate one. This brings us to the age-old question of choosing a local LLM: is **quantization level** or **parameter count** more important?

If I allocate a budget of **300 MB** for the model, I can use either a model like **LFM 2.5 350M** (which is 219.3 MB) or **Bonsai 1.7B** (which is 248.3 MB). Both models work and can call tools correctly. So which is better? I guess this question comes down to testing and preferences.

The LFM model is faster; I can get over 500 tokens per second. Meanwhile **Bonsai 1.7B** is closer to 250 tokens per second. I feel like Bonsai 1.7B tends to be **more reliable**—occasionally the 350M model can make some sort of mistake that slows me down (like an incorrect tool argument or something). So at least for myself, I feel like models with more parameters that are quantized down are a better option, due to increased reliability; but other use cases may favor the lower-parameter models (they are faster).