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text model · Qwen3-Coder · macOS

Can I run Qwen3-Coder 30B-A3B on Apple M4 (24GB)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Qwen3-Coder 30B-A3B needs ~19.4 GB even at Q4_K_M, but Apple M4 (24GB) only has ~16 GB usable.

Needs ~19.4 GB Device usable ~16 GB

Needs ~19.4 GB even at Q4_K_M, but only ~16 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 3.4 GB: Qwen3-Coder 30B-A3B needs roughly 19.4 GB at Q4_K_M and Apple M4 (24GB) leaves only about 16 GB usable for a model. The lightest tracked hardware that runs Qwen3-Coder 30B-A3B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Qwen3-Coder 30B-A3B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed
~19.4 GB
Usable on device
~16 GB
Device memory
24 GB
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Which quant fits

Quant ladder vs ~16 GB usable
Q2_K
~14.9 GB
Q3_K_M
~17 GB
Q4_K_M
~19.4 GB
Q5_K_M
~23.8 GB
Q6_K
~27.1 GB
Q8_0
~32.4 GB
FP16
~63.2 GB
The line marks Apple M4 (24GB)'s ~16 GB budget; rungs past it are too large.
Model Qwen3-Coder
Parameters
30.5B (MoE, 3.3B active)
Q4_K_M size
17.28 GB
Q8_0 size
30.25 GB
Context
256k
Ollama tag
qwen3-coder:30b
Full Qwen3-Coder 30B-A3B requirements →
Device macOS
Memory
24 GB unified
Usable for weights
~16 GB
Power draw
~65 W
Best runtime
Ollama (MLX backend, preview) / MLX direct
Best models for Apple M4 (24GB) →

What you can run instead

Run Qwen3-Coder 30B-A3B on other hardware

FAQ

Can Apple M4 (24GB) run Qwen3-Coder 30B-A3B?

No. Qwen3-Coder 30B-A3B needs ~19.4 GB even at Q4_K_M, but Apple M4 (24GB) only has ~16 GB usable.

How much memory does Qwen3-Coder 30B-A3B need?

Apple M4 (24GB) does not have enough memory. At Q4_K_M the weights are ~17.28 GB; with KV cache and runtime overhead, budget ~19.4 GB at a 4k context. It is a Mixture-of-Experts model (30.5B total / 3.3B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Qwen3-Coder 30B-A3B on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.