What’s the fastest way to run LLMs on Apple Silicon?

Ollama 0.19+ with OLLAMA_MLX=1 on 32GB+ M4—93% decode boost, no fuss.

Best quantization for quality-speed balance on Macs?

Q4_K_M: 75% smaller, 3.3% quality loss, perfect for bandwidth-bound inference.

Does MLX beat llama.cpp for all models?

No—crushes under 14B (20-87% faster), ties at 27B+, loses on long-prompt TTFT.

Your Mac Just Became an AI Beast: MLX Unlocks 87% Speedups on Apple Silicon

Tired of sluggish local LLMs? Apple Silicon's MLX engine delivers 20-87% faster inference, turning your Mac into a tokens-per-second monster. Everyday devs, rejoice—blazing AI is finally local.

theAIcatchup Apr 10, 2026 3 min read

Benchmark chart showing MLX outperforming llama.cpp by 87% on M4 Max Apple Silicon

⚡ Key Takeaways

MLX delivers 20-87% faster inference than llama.cpp on Apple Silicon for models under 14B. 𝕏
Memory bandwidth, not cores, sets your tok/s ceiling—quantize to Q4_K_M for max gains. 𝕏
Ollama 0.19+ auto-enables MLX on 32GB+ Macs, making elite performance effortless. 𝕏

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#LLM inference #MLX framework #Ollama optimization #apple silicon #mlx #ollama

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