Redhatai Llama 3.3 70B Instruct FP8 Dynamic speed on NVIDIA H100 PCIe 80GB and quantization-level VRAM fit.
NVIDIA H100 PCIe 80GB meets 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.
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Redhatai llama 3.3 70b instruct fp8 dynamic speed on nvidia h100 pcie 80gb
Redhatai Llama 3.3 70B Instruct FP8 Dynamic runs on NVIDIA H100 PCIe 80GB at about 110 tok/s at Q4 in our current compatibility dataset.
Based on 182 impressions tracked in Search Console.
NVIDIA H100 PCIe 80GB can run Redhatai Llama 3.3 70B Instruct FP8 Dynamic with Q4 quantization. At approximately 110 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 45GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 35GB | 80GB | 109.99 tok/s | ✅ Fits comfortably |
| Q8 | 70GB | 80GB | 76.99 tok/s | ✅ Fits comfortably |
| FP16 | 140GB | 80GB | 41.80 tok/s | ❌ Not recommended |
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NVIDIA H100 PCIe 80GB can run Redhatai Llama 3.3 70B Instruct FP8 Dynamic at Q4 with an estimated 110 tok/s.
Q4 inference is estimated to need about 35GB VRAM on this page, while NVIDIA H100 PCIe 80GB has 80GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.