Redhatai Meta Llama 3.1 70B Instruct Quantized.w4a16 speed on RTX 4080 Super and quantization-level VRAM fit.
RTX 4080 Super does not meet the minimum VRAM requirement for Q4 inference of Redhatai Meta Llama 3.1 70B Instruct Quantized.w4a16. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 4080 Super lacks sufficient VRAM for comfortable Redhatai Meta Llama 3.1 70B Instruct Quantized.w4a16 operation with Q4 quantization.
Your 16GB GPU is 20GB short of the 36GB 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 | 36GB | 16GB | 25.34 tok/s | ❌ Not recommended |
| Q8 | 71GB | 16GB | 17.74 tok/s | ❌ Not recommended |
| FP16 | 142GB | 16GB | 9.63 tok/s | ❌ Not recommended |
Check current pricing links for RTX 4080 Super and similar cards.
Open RTX 4080 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 Redhatai Meta Llama 3.1 70B Instruct Quantized.w4a16 on cloud GPU instantly.
RTX 4080 Super is not a comfortable Q4 fit for Redhatai Meta Llama 3.1 70B Instruct Quantized.w4a16 (about 36GB needed).
Q4 inference is estimated to need about 36GB VRAM on this page, while RTX 4080 Super has 16GB available.
Try lower-bit quantization, choose a smaller model, or move to a higher-VRAM GPU from the alternatives list.