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Local LLM ModelsRevisionas of Sunday, 26 July 2026 at 22:17 UTC
On an M2 MacBook Air with 24GB memory. There’s llmfit which I found to be extremely helpful and useful.
Running Models Locally
TODO: There’s a host of options.
Formats
TODO: MLX, GGUF, etc
Nomenclature
TODO: Reading files….
Llama.cpp
This is what I’m using. Homepage and list of models. It has a nice Web Interface.
# Start a web UI
llama-server -hf bartowski/Qwen_Qwen3.5-2B-GGUF:Q4_K_M
# Start a CLI
llama-cli -hf bartowski/Qwen_Qwen3.5-2B-GGUF:Q4_K_M
Trials
bartowski/Qwen_Qwen3.5-2B-GGUF– First one! Was fine.bartowski/Qwen_Qwen3.5-9B-GGUF– Slower but fine.bartowski/Qwen_Qwen3.6-27B-GGUF– Did not run. Memory problems. Expected.
Model Terminology
TODO:
Other
Models are stored in ~/.cache/huggingface.
Pi Agent Harness
This is a really cool project. You can type /model and switch models easily (i.e. you’re not just restricted to Claude Code, for instance) and this includes local models. Installed via
npm install -g --ignore-scripts @earendil-works/pi-coding-agent
# Now link it to Llama and it will autodiscover local models!
# First serve the models. Note the host and port.
llama serve --host localhost --port 8080
# Then install the extension
pi install git:github.com/huggingface/pi-llama
# Now run
pi
You will see the local models when you type /models and search for “llama-cpp”. Here’s a good post on using it. You get very few tools and are expected to build the rest and place them in $HOME/.pi.
Resources
- https://huggingface.co/bartowski
- https://huggingface.co/Brooooooklyn
- Friends Don’t Let Friends Use Ollama – Not sure what it offers above llama.cpp tbh. Great article with history.
- Running local models on an M4 with 24GB memory