作者： Jakub Rusinowski · 最后更新： 2025年4月28日
阿里巴巴的下一代模型系列，具备突破性的混合思维模式。Qwen 3模型可按需切换快速响应模式和深度思维链推理——输入/think处理复杂问题，/no_think获得快速答案。提供密集型和MoE变体。
| Licence | What it permits | Applies to |
|---|---|---|
Apache-2.0 | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | Qwen 3 8B, Qwen 3 14B, Qwen 3 32B, Qwen 3 30B-A3B (MoE), Qwen 3 235B-A22B (MoE) |
| Qwen 3 8B | Min 6 GB VRAM · Q4_K_M · 128,000 ctx · ollama run qwen3:8b |
| Qwen 3 14B | Min 10 GB VRAM · Q4_K_M · 128,000 ctx · ollama run qwen3:14b |
| Qwen 3 32B | Min 21 GB VRAM · Q4_K_M · 128,000 ctx · ollama run qwen3:32b |
| Qwen 3 30B-A3B (MoE) | Min 19 GB VRAM · Q4_K_M · 128,000 ctx · ollama run qwen3:30b-a3b |
| Qwen 3 235B-A22B (MoE) | Min 143 GB VRAM · Q4_K_M · 128,000 ctx · ollama run qwen3:235b-a22b |
The cheapest GPU that runs Qwen 3 locally (min 6 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run qwen3:8b
Minimum VRAM: 6 GB. For best results use Q4_K_M quantization.
Qwen 3 needs about 6 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Qwen 3 8B (6 GB, Q4_K_M); Qwen 3 14B (10 GB, Q4_K_M); Qwen 3 32B (21 GB, Q4_K_M); Qwen 3 30B-A3B (MoE) (19 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — Qwen 3 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.
Q4_K_M is the best balance of quality and VRAM for Qwen 3 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.
Install Ollama, then run: ollama run qwen3:8b. This downloads Qwen 3 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.