Can I Run Qwen3-Coder 30B on Mac Mini M4 Pro 48GB? Local Coding Sizing
Model weights, initial KV-cache, and CUDA context buffers.
Available safety margin for OS compositing and context expansion.
Throughput determined by memory bus bandwidth.
Hardware Physics & Bottleneck Analysis
Qwen3-Coder 30B-A3B activates only 3.3B parameters per token from its 30.5B MoE architecture across 48 layers. Apple's M4 Pro delivers 273 GB/s unified memory bandwidth. Because the GPU and CPU share the same physical DRAM pool, agentic coding tools like Claude Code, Cursor, or Aider ingest multi-file repository contexts without PCIe transfer lag.
Increase the macOS Metal allocation limit by running `sudo sysctl iogpu.wired_limit_mb=40960` in Terminal. This expands allocatable unified memory from 36GB to 40GB, allowing 64k repository context windows with zero swap.
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Frequently Asked Questions
Is MLX or llama.cpp faster for Qwen3-Coder on M4 Pro?
Apple's native `mlx-lm` generates ~42–48 tok/s on M4 Pro, whereas llama.cpp GGUF Metal achieves ~35–38 tok/s. Both run smoothly and silently on desktop power.
Can an M4 Pro with 24GB unified memory run Qwen3-Coder 30B?
A 24GB Mac Mini has only 18GB Metal allocatable capacity (~75%), falling ~2.7 GB short of the 20.66 GB footprint. It will aggressively swap to SSD unless quantized to 3-bit IQ3_M.
Calculate Exact Context Window Headroom
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