Qwen3-Coder — local AI model by Alibaba Cloud
Written by Jakub Rusinowski · Last updated
Alibaba's dedicated coding line. The 480B-A35B flagship holds state-of-the-art among open models on SWE-bench Verified and agentic coding at release (Qwen positions it comparable to Claude Sonnet 4), while the 80B-A3B variant delivers most of that quality with only 3B active parameters — cheap enough to self-host. Best-in-class open weights for repo-level coding, debugging, and code review.
Variants
The smallest Qwen3-Coder variant needs about 6 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.
| Model | VRAM at Q4 | VRAM | Context | Run it |
|---|---|---|---|---|
| Qwen3-Coder 8B 8B | ~5.6 GB | 128,000 | ollama run qwen3-coder:8b | |
| Qwen3-Coder 30B-A3B (MoE) → 30.5B (3.3B active) | ~19.2 GB | 262,144 | ollama run qwen3-coder:30b | |
| Qwen3-Coder-Next (80B-A3B MoE) → 80B/3B active | ~49.1 GB | 262,144 | ollama run qwen3-coder:80b-a3b-q4 | |
| Qwen3-Coder 480B-A35B (MoE) → 480B/35B active | ~290.6 GB | 256,000 | ollama run qwen3-coder:480b-a35b |
Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.
How to run Qwen3-Coder locally
Install Ollama, then pull the tag.
ollama run qwen3-coder:8bPick a size above for its own VRAM figure, speed estimate and install command.
Licence
Commercial use permitted. No usage restrictions beyond attribution.
Applies to: Qwen3-Coder 8B, Qwen3-Coder 30B-A3B (MoE), Qwen3-Coder-Next (80B-A3B MoE), Qwen3-Coder 480B-A35B (MoE)Recommended GPU
The cheapest catalogued GPU that runs Qwen3-Coder locally (min 6 GB VRAM) is the Intel Arc B570 (10 GB).
Can I run Qwen3-Coder on my GPU?
- Qwen3-Coder on AMD Radeon RX 9060 XT 8GB
- Qwen3-Coder on AMD Ryzen AI Max+ 395
- Qwen3-Coder on Apple M2 Max
- Qwen3-Coder on Apple M3 Ultra
- Qwen3-Coder on Intel Arc B570
- Qwen3-Coder on Intel Arc B580
- Qwen3-Coder on NVIDIA A100 80GB (PCIe)
- Qwen3-Coder on NVIDIA GeForce RTX 3060 (12GB)
- Qwen3-Coder on NVIDIA GeForce RTX 3070
- Qwen3-Coder on NVIDIA GeForce RTX 3070 Ti
- Qwen3-Coder on NVIDIA GeForce RTX 3080 (10GB)
- Qwen3-Coder on NVIDIA GeForce RTX 4060
- Qwen3-Coder on NVIDIA GeForce RTX 4070
- Qwen3-Coder on NVIDIA GeForce RTX 4070 Super
- Qwen3-Coder on NVIDIA GeForce RTX 4070 Ti
- Qwen3-Coder on NVIDIA GeForce RTX 5060
- Qwen3-Coder on NVIDIA GeForce RTX 5060 Ti 8GB
- Qwen3-Coder on NVIDIA GeForce RTX 5070
- Qwen3-Coder on NVIDIA H100 80GB (PCIe)
- Qwen3-Coder on NVIDIA RTX PRO 6000 Blackwell
Qwen3-Coder — frequently asked questions
How much VRAM does Qwen3-Coder need?
Qwen3-Coder needs about 6 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Qwen3-Coder 8B (6 GB, Q4_K_M); Qwen3-Coder 30B-A3B (MoE) (19 GB, Q4_K_M); Qwen3-Coder-Next (80B-A3B MoE) (49 GB, Q4_K_M); Qwen3-Coder 480B-A35B (MoE) (291 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Qwen3-Coder on an RTX 4090 (24 GB)?
Yes — Qwen3-Coder runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
What quantization should I use for Qwen3-Coder?
Q4_K_M is the best balance of quality and VRAM for Qwen3-Coder in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run Qwen3-Coder with Ollama?
Install Ollama, then run: ollama run qwen3-coder:8b. This downloads Qwen3-Coder and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.