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text model · Qwen3-Coder · macOS

Can I run Qwen3-Coder 30B-A3B on Apple M5 (32GB)?

Compatibility verdict VRAM threshold engine
Yes, but tight GPU accelerated

Yes. Qwen3-Coder 30B-A3B runs on Apple M5 (32GB) at Q4_K_M (~19.4 GB of ~21 GB usable).

Needs ~19.4 GB Device usable ~21 GB

Fits at Q4_K_M (~19.4 GB of ~21 GB usable) but with little headroom. Close other apps, or drop to a 2k context to free about a gigabyte.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 (32GB) leaves ~1.6 GB of headroom.

Q4_K_M needed
~19.4 GB
Usable on device
~21 GB
Device memory
32 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~21 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 M5 (32GB)'s ~21 GB budget; rungs past it are too large.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama
$ ollama run qwen3-coder:30b
llama.cpp
$ llama-cli -hf unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M
LM Studio
$ lms get unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF
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
32 GB unified
Usable for weights
~21 GB
Best runtime
MLX direct / Ollama (MLX backend)
Best models for Apple M5 (32GB) →

You could also run

Run Qwen3-Coder 30B-A3B on other hardware

FAQ

Can Apple M5 (32GB) run Qwen3-Coder 30B-A3B?

Yes. Qwen3-Coder 30B-A3B runs on Apple M5 (32GB) at Q4_K_M (~19.4 GB of ~21 GB usable).

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

It is a tight fit on Apple M5 (32GB). 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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Markdown
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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.