MLX LM vs Ollama

MLX LM

MIT, with no telemetry found in the source, and the tests passed on the last eight pushes to main. mlx_lm.server has no API key option, answers any origin by default and loads whichever model a request names, and its own docs say it is not recommended for production.

Ollama

An OpenAPI 3.1 file for the 15 native operations and llms.txt with 68 links to Markdown pages. No credential on the local API, and any caller that reaches it can pull, push, create and delete models.

MLX LM

1. Keep mlx_lm.server on 127.0.0.1 and pass --allowed-origins with the origins you trust. There is no API key, and the default answers every origin
2. Treat any caller as able to load any model. The model and adapters request fields accept any Hugging Face repository or local path
3. Send max_tokens or max_completion_tokens when you need more than 512 tokens, the server default
4. Read errors as {"error": "<text>"} with 400 for a bad field and 404 for a model that failed to load. They are not OpenAI error objects
5. Poll GET /health before the first request. It answers 503 with unavailable when the generation thread has stopped

Ollama

1. Send "stream": false for one JSON body. The native routes stream NDJSON by default
2. Set OLLAMA_CONTEXT_LENGTH=64000 or options.num_ctx before agent work. The default is 4k below 24 GiB of VRAM
3. Back off on a 503. It means the queue (512 by default) is full
4. Put an authenticating proxy in front before binding past 127.0.0.1. The server checks no credential
5. Expect model names with a cloud tag to run on Ollama's servers. They need ollama signin and fail with OLLAMA_NO_CLOUD=1