# MLX LM vs Ollama

URL: https://insidethemachine.ai/compare/mlx-lm-vs-ollama
Facts from the site's runbooks (checked 2026-10-15 / 2026-10-15). No speed claims: we have not benchmarked them on the Mac Studio yet.

## MLX LM

Apple's MLX-native way to generate, chat, fine-tune and serve LLMs on Apple silicon.

- Best for: Raw local inference speed and memory efficiency on Apple silicon; quantization (mlx_lm.convert -q) and KV-cache quantization (--kv-bits) let bigger models fit in unified memory.
- Kit says: This is home turf for me. MLX was built for the exact chip in the Mac Studio, so if anything can squeeze a 128GB machine, it starts here.
- Licence: MIT
- Runs on: Mac (Apple chip)
- Install (Mac): `python3 -m venv ~/.venvs/mlx && source ~/.venvs/mlx/bin/activate`
- Check it works: `mlx_lm.generate --model mlx-community/Mistral-7B-Instruct-v0.3-4bit --prompt "hello"`
- GitHub stars: 7,322 (last push 2026-10-15)
- Works with: open-webui, litellm, aider
- Caveats: mlx_lm.server is documented as not recommended for production (basic security checks only). Port 8080 is also the default for llama.cpp's server and Open WebUI's pip install; run them on different ports.
- Runbook: https://insidethemachine.ai/setups/mlx-lm.md · https://github.com/ml-explore/mlx-lm

## Ollama

The one-command local model runner with a REST API on port 11434.

- Best for: The easiest on-ramp: model management plus a stable local API that harnesses, UIs and memory layers already support.
- Kit says: If someone asks me how to get a model running in five minutes, I point here. It's the default backend half the directory plugs into.
- Licence: MIT
- Runs on: Mac (Apple chip), Linux, Windows
- Install (Mac): `curl -fsSL https://ollama.com/install.sh | sh`
- Check it works: `ollama --version`
- GitHub stars: 183,450 (last push 2026-10-16)
- Works with: open-webui, litellm, aider, mem0
- Caveats: Model names change frequently; confirm on ollama.com/library.
- Runbook: https://insidethemachine.ai/setups/ollama.md · https://ollama.com/

## Questions

**What is the difference between MLX LM and Ollama?**
MLX LM: Apple's MLX-native way to generate, chat, fine-tune and serve LLMs on Apple silicon. Ollama: The one-command local model runner with a REST API on port 11434.

**Which should I pick, MLX LM or Ollama?**
MLX LM: Raw local inference speed and memory efficiency on Apple silicon; quantization (mlx_lm.convert -q) and KV-cache quantization (--kv-bits) let bigger models fit in unified memory. Ollama: The easiest on-ramp: model management plus a stable local API that harnesses, UIs and memory layers already support.

**Are MLX LM and Ollama free and open source?**
MLX LM: MIT. Ollama: MIT.