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) | ~55 tokens/sec |
All models below run comfortably in 8 GB VRAM with Q4_K_M quantization.
| Llama 3.2 Family | 8 GB VRAM · Q4_K_M · ollama run llama3.2-vision:11b |
| Llama 3.1 Family | 6 GB VRAM · Q4_K_M · ollama run llama3.1 |
| Qwen 2.5 Family | 5 GB VRAM · Q4_K_M · ollama run qwen2.5:7b |
| Gemma 2 Family | 6 GB VRAM · Q4_K_M · ollama run gemma2 |
| Phi-4 Mini | 3 GB VRAM · Q4_K_M · ollama run phi4-mini |
| Mistral Family | 8 GB VRAM (smallest variant — needs more VRAM or a lower quant) · Q4_K_M · ollama run mistral-nemo |
| SmolLM2 | 1 GB VRAM · Q4_K_M · ollama run smollm2:360m |
| DeepSeek R1 | 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 runs Llama 3.1 8B at ~55 tokens/sec with Q4_K_M quantization.
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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