Written by Jakub Rusinowski · Last updated July 12, 2026
12 GB VRAM handles 7–8B models well and some 13B models with aggressive Q4 quantization. Great everyday AI card.
| VRAM | 12 GB |
| Memory Bandwidth | 504 GB/s |
| TDP | 200 W |
| Architecture | Ada Lovelace AD104 |
| Release Year | 2023 |
| MSRP at Launch | $599 |
| Inference Speed (Llama 3.1 8B Q4_K_M) | 50–96 tok/s (estimated) |
| Inference Speed (Llama 3.3 70B Q4_K_M) | Does not fit — needs ~44 GB of 12 GB usable |
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All models below run comfortably in 12 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 |
| Llama 3.2 Family | Llama 3.2 11B Vision Instruct · 7 GB VRAM · Q4_K_M · llama-3-2 |
| Qwen 2.5 Family | Qwen 2.5 14B Instruct · 9 GB VRAM · Q4_K_M · ollama run qwen2.5:14b |
| Gemma 3 | Gemma 3 12B Instruct · 8 GB VRAM · Q4_K_M · ollama run gemma3:12b |
| Phi-4 Mini | Phi-4 Mini (3.8B) · 3 GB VRAM · Q4_K_M · ollama run phi4-mini |
| Mistral Family | Mistral NeMo 12B · 8 GB VRAM · Q4_K_M · ollama run mistral-nemo |
| SmolLM2 | SmolLM2 1.7B Instruct · 2 GB VRAM · Q4_K_M · ollama run smollm2:1.7b |
Install Ollama then run the recommended model for this GPU:
ollama run llama3.1:8b
Yes — the NVIDIA GeForce RTX 4070 has 12 GB VRAM and runs 12 GB VRAM handles 7–8B models well and some 13B models with aggressive Q4 quantization. Great everyday AI card.
The NVIDIA GeForce RTX 4070 is estimated to run Llama 3.1 8B at 50–96 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 12 GB usable. These are modelled estimates, not measurements — see /en/methodology.
With 12 GB you can run: Llama 3.1 Family, Llama 3.2 Family, Qwen 2.5 Family, Gemma 3, Phi-4 Mini. Use Ollama for the easiest setup: ollama run llama3.1:8b.
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