作者： Jakub Rusinowski · 最后更新： 2026年7月12日
最快的消费级AI GPU。32 GB VRAM可在单卡上运行Qwen 3 32B和带Q3量化的Llama 3.3 70B。比RTX 4090快67%。
| VRAM | 32 GB |
| Memory Bandwidth | 1792 GB/s |
| TDP | 575 W |
| Architecture | Blackwell GB202 |
| Release Year | 2025 |
| MSRP at Launch | $1,999 |
| Inference Speed (Llama 3.1 8B Q4_K_M) | 113–234 tok/s (estimated) |
| Inference Speed (Llama 3.3 70B Q4_K_M) | Does not fit — needs ~44 GB of 32 GB usable |
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All models below run comfortably in 32 GB VRAM with Q4_K_M quantization.
| Llama 3.1 Family | Llama 3.1 8B Instruct · 6 GB VRAM · Q4_K_M · ollama run llama3.1 |
| DeepSeek R1 | DeepSeek R1 Distill Qwen 32B · 20 GB VRAM · Q4_K_M · ollama run deepseek-r1:32b |
| Qwen 3 | Qwen 3 32B · 21 GB VRAM · Q4_K_M · ollama run qwen3:32b |
| Qwen 3.5 | Qwen 3.5 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.5:35b-a3b |
| Qwen 3.6 | Qwen 3.6 35B-A3B · 22 GB VRAM · Q4_K_M · ollama run qwen3.6:35b-a3b |
| Qwen 3.7 | Qwen 3.7 35B-A3B · 22 GB VRAM · Q4_K_M · qwen3-7 |
| Gemma 3 | Gemma 3 27B Instruct · 17 GB VRAM · Q4_K_M · ollama run gemma3:27b |
| Gemma 4 | Gemma 4 31B · 20 GB VRAM · Q4_K_M · ollama run gemma4:31b |
Install Ollama then run the recommended model for this GPU:
ollama run llama4:scout
Yes — the NVIDIA GeForce RTX 5090 has 32 GB VRAM and runs 最快的消费级AI GPU。32 GB VRAM可在单卡上运行Qwen 3 32B和带Q3量化的Llama 3.3 70B。比RTX 4090快67%。
The NVIDIA GeForce RTX 5090 is estimated to run Llama 3.1 8B at 113–234 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 32 GB usable. These are modelled estimates, not measurements — see /en/methodology.
With 32 GB you can run: Llama 3.1 Family, DeepSeek R1, Qwen 3, Qwen 3.5, Qwen 3.6. Use Ollama for the easiest setup: ollama run llama4:scout.
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