Redhatai Llama 3.3 70B Instruct FP8 Dynamic speed on RTX 5080 and quantization-level VRAM fit.
RTX 5080 does not meet the minimum VRAM requirement for Q4 inference of Redhatai Llama 3.3 70B Instruct FP8 Dynamic. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 5080 lacks sufficient VRAM for comfortable Redhatai Llama 3.3 70B Instruct FP8 Dynamic operation with Q4 quantization.
Your 16GB GPU is 19GB short of the 35GB 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 | 35GB | 16GB | 55.34 tok/s | ❌ Not recommended |
| Q8 | 70GB | 16GB | 38.74 tok/s | ❌ Not recommended |
| FP16 | 140GB | 16GB | 21.03 tok/s | ❌ Not recommended |
Check current pricing links for RTX 5080 and similar cards.
Open RTX 5080 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 →RTX 5080 is not a comfortable Q4 fit for Redhatai Llama 3.3 70B Instruct FP8 Dynamic (about 35GB needed).
Q4 inference is estimated to need about 35GB VRAM on this page, while RTX 5080 has 16GB available.
Try lower-bit quantization, choose a smaller model, or move to a higher-VRAM GPU from the alternatives list.