RUNTIME / MEDIUM
MLX LM
Native Apple silicon inference, experimentation and fine-tuning
Sumber primerDifficultyMediumSetup profile
Platforms1apple
APIOpenAI-compatible server + CLI:8080
01 / INSTALL & RUN
Installation depends on your OS. These commands are examples or templates: replace placeholders and verify quantization, file format, drivers and runtime support. Memory fit does not guarantee deployment.
- 1Install
pip install mlx-lm - 2Start a model
mlx_lm.server --model <verified-MLX-conversion> - 3Connect your app
OpenAI-compatible server + CLI · :8080. Keep the service bound to localhost unless you add authentication and network controls.
Perhatian
Only for Apple silicon. Use an MLX-converted model and tune KV cache size for long contexts. The bundled mlx_lm.server is an OpenAI-compatible HTTP endpoint (default 127.0.0.1:8080), but upstream warns it implements only basic security checks and is not recommended for production.
02 / COMPATIBLE MODELS
Model open-weight perwakilan
Alibaba Qwen
Qwen3.6 27B
27BQ4 / NVFP4 estimate
- Estimasi memori
- ~18.5 GB
- Konteks
- 262K native
Alibaba Qwen
Qwen3.8 27B
27BQ4 estimate
- Estimasi memori
- ~19.5 GB
- Konteks
- 262K native
Google
Gemma 3 1B
1BINT4 / GGUF Q4
- Estimasi memori
- ~1.4 GB
- Konteks
- 32K
Meta
Llama 3.2 3B
3BGGUF Q4
- Estimasi memori
- ~2.8 GB
- Konteks
- 128K
Alibaba Qwen
Qwen3 4B
4BGGUF Q4
- Estimasi memori
- ~3.6 GB
- Konteks
- 32K+
Google
Gemma 3 4B
4BINT4 / GGUF Q4
- Estimasi memori
- ~4.2 GB
- Konteks
- 128K