# Small Models for Agentic Systems > Source: > Published: 2026-08-20 07:00:00+00:00 Last night I spent some time trying to make my **MCP server** work with a **smaller local model**, one that is just 1.2 billion parameters and 696 MB on disk. This is LiquidAI's LFM 2.5 1.2B, with file name `LFM2.5-1.2B-Instruct-QAD-Q4_0.gguf` . Most small models I test fail too badly and I just **delete them** right away. However this model seemed to have some potential to work for my agentic system. It **required some changes** in the MCP server. • I had to add more examples and details about how to respond on certain user queries. • I had to remove a confusing tool (which it did not seem to understand, and would randomly call just to annoy me). So basically I have a system with just **1 tool** that can be called in many different ways. The **small model** thus **does not get confused** about what it is supposed to do. It still gets confused by certain things: it has trouble keeping track of who said what, and what certain words mean in a phrase. However it runs at over 300 tokens per second, uses minimal RAM, and will make it so I basically will never need to update my GPU to run local agentic tasks (it even will run speedily on CPU alone).