Written by Jakub Rusinowski · Last updated July 12, 2026
Great entry-level AI GPU. 8 GB VRAM is enough for any 7–8B model in Q4 quantization. Only 115W TDP makes it ideal for always-on AI servers.
| 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 Great entry-level AI GPU. 8 GB VRAM is enough for any 7–8B model in Q4 quantization. Only 115W TDP makes it ideal for al
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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