Redhatai Llama 3.2 90B Vision Instruct FP8 Dynamic speed on RTX 4070 Ti Super and quantization-level VRAM fit.
RTX 4070 Ti Super does not meet the minimum VRAM requirement for Q4 inference of Redhatai Llama 3.2 90B Vision Instruct FP8 Dynamic. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 4070 Ti Super lacks sufficient VRAM for comfortable Redhatai Llama 3.2 90B Vision Instruct FP8 Dynamic operation with Q4 quantization.
Your 16GB GPU is 29GB short of the 45GB 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 | 45GB | 16GB | 22.64 tok/s | ❌ Not recommended |
| Q8 | 90GB | 16GB | 15.85 tok/s | ❌ Not recommended |
| FP16 | 180GB | 16GB | 8.60 tok/s | ❌ Not recommended |
Check current pricing links for RTX 4070 Ti Super and similar cards.
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Compare prebuilt systems →Your GPU doesn't meet the VRAM requirements. Run Redhatai Llama 3.2 90B Vision Instruct FP8 Dynamic on cloud GPU instantly.
RTX 4070 Ti Super is not a comfortable Q4 fit for Redhatai Llama 3.2 90B Vision Instruct FP8 Dynamic (about 45GB needed).
Q4 inference is estimated to need about 45GB VRAM on this page, while RTX 4070 Ti Super has 16GB available.
Try lower-bit quantization, choose a smaller model, or move to a higher-VRAM GPU from the alternatives list.