作者： Jakub Rusinowski · 最后更新： 2026年7月12日
出色的入门级AI GPU。8 GB VRAM足以在Q4量化下运行任何7–8B模型。仅115W TDP，非常适合全天候AI服务器。
| VRAM | 8 GB |
| Memory Bandwidth | 272 GB/s |
| TDP | 115 W |
| Architecture | Ada Lovelace AD107 |
| Release Year | 2023 |
| MSRP at Launch | $299 |
| Inference Speed (Llama 3.1 8B Q4_K_M) | 29–56 tok/s (estimated) |
| Inference Speed (Llama 3.3 70B Q4_K_M) | Does not fit — needs ~44 GB of 8 GB usable |
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All models below run comfortably in 8 GB VRAM with Q4_K_M quantization.
| Llama 3.2 Family | Llama 3.2 11B Vision Instruct · 7 GB VRAM · Q4_K_M · llama-3-2 |
| Llama 3.1 Family | Llama 3.1 8B Instruct · 6 GB VRAM · Q4_K_M · ollama run llama3.1 |
| Qwen 2.5 Family | Qwen 2.5 7B Instruct · 5 GB VRAM · Q4_K_M · ollama run qwen2.5:7b |
| Gemma 2 Family | Gemma 2 9B IT · 6 GB VRAM · Q4_K_M · ollama run gemma2 |
| Phi-4 Mini | Phi-4 Mini (3.8B) · 3 GB VRAM · Q4_K_M · ollama run phi4-mini |
| SmolLM2 | SmolLM2 1.7B Instruct · 2 GB VRAM · Q4_K_M · ollama run smollm2:1.7b |
| DeepSeek R1 | DeepSeek R1 Distill Llama 8B · 6 GB VRAM · Q4_K_M · ollama run deepseek-r1:8b |
Install Ollama then run the recommended model for this GPU:
ollama run llama3.1:8b
Yes — the NVIDIA GeForce RTX 4060 has 8 GB VRAM and runs 出色的入门级AI GPU。8 GB VRAM足以在Q4量化下运行任何7–8B模型。仅115W TDP，非常适合全天候AI服务器。
The NVIDIA GeForce RTX 4060 is estimated to run Llama 3.1 8B at 29–56 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 8 GB usable. These are modelled estimates, not measurements — see /en/methodology.
With 8 GB you can run: Llama 3.2 Family, Llama 3.1 Family, Qwen 2.5 Family, Gemma 2 Family, Phi-4 Mini. Use Ollama for the easiest setup: ollama run llama3.1:8b.
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