What is MLX for Apple Silicon LLM inference ?

Apple's open-source framework tuned for unified memory – crushes llama.cpp by optimizing Metal shaders and bandwidth for models under 14B.

How do I enable MLX in Ollama?

Update to 0.19+, set `export OLLAMA_MLX=1`, run `ollama run [model]`. Auto-activates on 32GB+ Macs.

Is MLX faster than llama.cpp on M4 Macs?

Yes, 20-87% for small/medium models (under 14B). Long prefill hurts TTFT; pick llama.cpp for RAG.

🤝 Community & Governance

MLX Unleashes 87% Faster LLM Inference on Apple Silicon – Your Max-Speed Playbook

Picture this: 525 tokens per second on a tiny Qwen model via MLX on M4 Max. That's 87% faster than llama.cpp – and it's just the start of Apple Silicon's local AI explosion.

theAIcatchup Apr 11, 2026 4 min read

Benchmark graph: MLX vs llama.cpp speeds on M4 Max Apple Silicon

⚡ Key Takeaways

MLX boosts inference 20-87% over llama.cpp on Apple Silicon for <14B models 𝕏
Ollama 0.19+ MLX backend delivers 93% faster decode with one env var 𝕏
Q4_K_M quantization: 75% size cut, 3.3% quality loss – bandwidth rules all 𝕏

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theAIcatchup

Community-driven. Code-first.

#Apple Silicon #LLM inference #LLM optimization #MLX #MLX inference #Ollama #Ollama MLX

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Originally reported by Dev.to

⚡ Key Takeaways

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