Alibaba
Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 21.9 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 3.299999952316284B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 26 GB of VRAM.
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ollama run qwen3-coderQuick specs
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.9 GB | Low | — |
Q3_K_S | 3 | 14.9 GB | Low | — |
NVFP4 | 4 | 17.1 GB | Medium | — |
Q4_K_M | 4 | 18.6 GB | Medium | — |
Q5_K_M | 5 | 22.0 GB | High | — |
Q6_K | 6 | 25.0 GB | High | — |
Q8_0 | 8 | 32.6 GB | Very High | — |
F16 | 16 | 62.5 GB | Maximum | — |
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Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 21.9 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Mac mini M4 64GB can run Qwen3-Coder 30B A3B Instruct with a compatibility score of 92/100. It provides 64 GB of memory and achieves approximately 13.1 tokens per second.
The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for Qwen3-Coder 30B A3B Instruct: RTX 5090 32GB (score: 100/100), RTX PRO 4500 Blackwell 32GB (score: 100/100), NVIDIA V100 32GB (score: 100/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Qwen3-Coder 30B A3B Instruct is well-suited for coding as well as reasoning, agentic. It was designed with these use cases in mind.
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