Codellama Codellama 34B HF speed on RTX 4070 Super and quantization-level VRAM fit.
RTX 4070 Super does not meet the minimum VRAM requirement for Q4 inference of Codellama Codellama 34B HF. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 4070 Super lacks sufficient VRAM for comfortable Codellama Codellama 34B HF operation with Q4 quantization.
Your 12GB GPU is 5GB short of the 17GB 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 | 17GB | 12GB | 30.52 tok/s | ❌ Not recommended |
| Q8 | 34GB | 12GB | 21.36 tok/s | ❌ Not recommended |
| FP16 | 68GB | 12GB | 11.60 tok/s | ❌ Not recommended |
Check current pricing links for RTX 4070 Super and similar cards.
Open RTX 4070 Super 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 Codellama Codellama 34B HF on cloud GPU instantly.
RTX 4070 Super is not a comfortable Q4 fit for Codellama Codellama 34B HF (about 17GB needed).
Q4 inference is estimated to need about 17GB VRAM on this page, while RTX 4070 Super has 12GB available.
Try lower-bit quantization, choose a smaller model, or move to a higher-VRAM GPU from the alternatives list.