BestHub
Sign in
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 18, 2026 · Artificial Intelligence

How to Run MiniMax‑M2.7 on Mac: Comparing Two Quantization Paths

This article explains why standard uniform quantization fails for the 228‑billion‑parameter MiniMax‑M2.7 MoE model on macOS, and compares two practical solutions—JANGTQ + MLX Studio with 2‑bit mixed‑precision achieving 91.5 % MMLU using 56.5 GB, and LM Studio + GGUF which is easier but requires at least 138 GB RAM and yields lower accuracy.

JANGTQLM StudioMLX Studio
0 likes · 8 min read
How to Run MiniMax‑M2.7 on Mac: Comparing Two Quantization Paths
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 1, 2026 · Artificial Intelligence

Running Large Models Locally on Mac: The Most Powerful Current Solution

This article reviews the JANG quantization format, the vMLX inference engine with a five‑layer cache stack, and the MLX Studio GUI, showing how their combination enables 397B‑parameter models to fit on 128 GB Apple Silicon Macs, achieve up to 224× faster first‑token latency for 100K context, and provide a full‑featured local AI experience.

Apple SiliconJANGMLX Studio
0 likes · 8 min read
Running Large Models Locally on Mac: The Most Powerful Current Solution