# Small Language Models (350M Parameters)

> Source: <https://www.dotnetperls.com/2026_8_22_small-language-models-350m>
> Published: 2026-08-22 07:00:00+00:00

I have had good results with **small language models** (such as 1.2B parameters) for interacting with my MCP server. I decided to try to see how small I could go—if 1.2B worked, would 350M work, would 230M? Each size decrease means **more efficiency**.

I have found LiquidAI's small models to be quite **effective** at calling tools. I could use the **1.2B** parameter model with minimal changes to the MCP server code. With **350M**, I had to **redesign** a feature and use a different approach, but it eventually worked as well.

Some notes:

• While 2.6B (`LFM2.5-2.6B-QAD-Q4_0.gguf`

) and 1.2B (`LFM2.5-1.2B-Instruct-QAD-Q4_0.gguf`

) work well with minimal modifications, 350M (`LFM2.5-350M-QAD-Q4_0.gguf`

) required fairly extensive code changes and rewrites.

• 230M (`LFM2.5-230M-QAD-Q4_0.gguf`

) does not appear viable for my use—it is simply too small.

• The performance improvements with small models are astonishing—a 1.2B model can do 300 tokens a second, while a 350M model can do 500 tokens a second.

How can one build a **program** that **works well** with a 350M model? Basically I recommend designing it from **scratch** to work with the model—test each feature, and only add features that the model can handle. A 350M parameter model has limitations; it is possible to build up a program that works with it though.