Deepseek AI Deepseek V2 5 speed on RTX 4070 Ti and quantization-level VRAM fit.
RTX 4070 Ti does not meet the minimum VRAM requirement for Q4 inference of Deepseek AI Deepseek V2 5. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 4070 Ti lacks sufficient VRAM for comfortable Deepseek AI Deepseek V2 5 operation with Q4 quantization.
Your 12GB GPU is 106GB short of the 118GB minimum.
Options: (1) Try Q2 or Q3 quantization for lower VRAM requirements, (2) Consider cloud GPU rental, (3) Upgrade to a GPU with at least 16GB VRAM.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 118GB | 12GB | 13.29 tok/s | ❌ Not recommended |
| Q8 | 236GB | 12GB | 9.30 tok/s | ❌ Not recommended |
| FP16 | 472GB | 12GB | 5.05 tok/s | ❌ Not recommended |
Check current pricing links for RTX 4070 Ti and similar cards.
Open RTX 4070 Ti buy links →Use workload-focused recommendations before committing to a purchase.
Browse best GPU guides →Compare complete systems if you want ready-to-run hardware.
Compare prebuilt systems →Your GPU doesn't meet the VRAM requirements. Run Deepseek AI Deepseek V2 5 on cloud GPU instantly.
RTX 4070 Ti is not a comfortable Q4 fit for Deepseek AI Deepseek V2 5 (about 118GB needed).
Q4 inference is estimated to need about 118GB VRAM on this page, while RTX 4070 Ti has 12GB available.
Try lower-bit quantization, choose a smaller model, or move to a higher-VRAM GPU from the alternatives list.