What hardware do I need to run Qwen3-Coder 30B-A3B?

At its most compressed (Q4_K_M) it needs roughly 18GB of VRAM, and about 24GB for good quality. VRAM figures are approximate and depend on context length and settings.

Is Qwen3-Coder 30B-A3B free for commercial use?

Yes. Qwen3-Coder 30B-A3B is licensed under Apache 2.0, which permits commercial use with no meaningful conditions.

Can I run Qwen3-Coder 30B-A3B on Apple Silicon?

Yes. Qwen3-Coder 30B-A3B has builds optimised for Apple Silicon, through MLX or GGUF on a Mac.

Does Qwen3-Coder 30B-A3B run on CPU?

Yes, Qwen3-Coder 30B-A3B can run on the CPU, though generation is slower than on a GPU.

What is Qwen3-Coder 30B-A3B's context window?

Qwen3-Coder 30B-A3B has a context window of 262,144 tokens, about 262k.

Qwen3-Coder 30B-A3B

Name: Qwen3-Coder 30B-A3B
Author: Alibaba

Alibaba · Code · MoE 30B-A3B · 262k context · Released 14 May 2025

Commercial use permitted Open weights Runs on CPU Apple Silicon

Qwen3-Coder 30B-A3B is purpose-built for agentic software engineering rather than chat. It runs in a non-thinking mode by design, activating 3B of its 30B parameters per token so it generates quickly, and its 256k context window (up to 1M with extrapolation) suits working across a whole repository. It is a practical local counterpart to the much larger Qwen3-Coder 480B.

Strengths

Strong at agentic coding, tool use, and multi-file edits
Very long 256k native context, useful for whole-repository work
Fast generation for its quality, thanks to the mixture-of-experts design
Apache 2.0, so no commercial-use conditions

Weaknesses

Needs the memory of a 30B model despite only 3B being active
Non-thinking by design, so it will not show step-by-step reasoning
Qwen does not publish headline scores on the card, so benchmark on your own code

Hardware requirements

Quantisation	Approx. VRAM	Notes
Q4_K_M	~18GB	Fits a 24GB card with some room for context
Q8_0	~32GB	Near-lossless, needs 36GB or more
FP16	~61GB	Full precision, server or multi-GPU territory

Also runs on CPU (slower). Optimised builds available for Apple Silicon.

What you'd need to run this

Roughly what a machine to run this would need, at up to three levels of quality. Memory is the deciding factor.

Minimum to run it

Q4_K_M · ~18GB needed

One 24GB GPU

NVIDIA Tesla P40

or a Mac or mini-PC with unified memory, if you prefer no discrete GPU, Mac mini M5 Pro .

32–64GB of system RAM alongside the card.

around £700–£1,100

What else 24GB runs →

For good quality

Q8_0 · ~32GB needed

64GB of unified memory

Mac mini M4 Pro

or a single high-memory workstation card, NVIDIA RTX 6000 Ada Generation .

Unified memory is shared with the model, so it is already counted above.

around £1,100–£2,000

See the Affordable Mac build build What else 64GB runs →

Best quality

FP16 · ~61GB needed

96GB of unified memory

AMD Ryzen AI Max+ 395 (Strix Halo)

or an 80GB-class data-centre card, which is usually rented by the hour, NVIDIA A100 80GB .

Unified memory is shared with the model, so it is already counted above.

around £1,100–£2,000

See the Quiet large-model box build What else 96GB runs →

Licence

Apache 2.0 — read the licence

Benchmarks

Benchmark	Score	Source	As of
SWE-bench Verified	51.6	Qwen (official repo, OpenHands scaffold, 100 turns)	August 2025

How it compares

How this model’s reported scores sit against other models we cover, on the same benchmarks. This model is highlighted.

SWE-bench Verified

higher is better

Muse Glimmer 30B 76.0%

Meta model card · August 2026
gpt-oss-120b 62.4%

OpenAI gpt-oss model card (high reasoning) · August 2025
gpt-oss-20b 60.7%

OpenAI gpt-oss model card (high reasoning) · August 2025
Devstral Small 53.6%

Mistral AI model card (Devstral Small 1.1) · July 2025
Qwen3-Coder 30B-A3B 51.6

Qwen (official repo, OpenHands scaffold, 100 turns) · August 2025
Nemotron 3.5 Lightning 51.56

NVIDIA (BF16) · August 2026

Qwen3-Coder 30B-A3B: common questions

What hardware do I need to run Qwen3-Coder 30B-A3B?: At its most compressed (Q4_K_M) it needs roughly 18GB of VRAM, and about 24GB for good quality. VRAM figures are approximate and depend on context length and settings.
Is Qwen3-Coder 30B-A3B free for commercial use?: Yes. Qwen3-Coder 30B-A3B is licensed under Apache 2.0, which permits commercial use with no meaningful conditions.
Can I run Qwen3-Coder 30B-A3B on Apple Silicon?: Yes. Qwen3-Coder 30B-A3B has builds optimised for Apple Silicon, through MLX or GGUF on a Mac.
Does Qwen3-Coder 30B-A3B run on CPU?: Yes, Qwen3-Coder 30B-A3B can run on the CPU, though generation is slower than on a GPU.
What is Qwen3-Coder 30B-A3B's context window?: Qwen3-Coder 30B-A3B has a context window of 262,144 tokens, about 262k.

Availability

Official page
Hugging Face
ollama run qwen3-coder:30b

Where to get quantised weights

Some of the best quantised weights are made by the community, not the model’s authors. Look this model up on these providers:

Unsloth GGUF (Dynamic 2.0, imatrix)
Dynamic and imatrix GGUF quants that often hold quality better than a plain quant at the same bit-width, especially at 4-bit and below.
Bartowski GGUF Q2-Q8 (imatrix)
A wide, reliable range of imatrix GGUF quants, typically Q2 through Q8.
MLX community MLX 4-bit and 8-bit
MLX quants for Apple Silicon, usually 4-bit and 8-bit.

Recommended for

Local agentic coding on a 24GB card
Working across large codebases where long context helps
A permissively licensed coding model for commercial work

Related models

Run it with

Related guides

Understanding quantisation

Glossary

Catalogue entry last verified 30 July 2026. Specifications change; verify anything you are about to spend money on.