Redhatai Llama 3.3 70B Instruct FP8 Dynamic speed on NVIDIA A100 80GB SXM4 and quantization-level VRAM fit.
NVIDIA A100 80GB SXM4 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.
NVIDIA A100 80GB SXM4 can run Redhatai Llama 3.3 70B Instruct FP8 Dynamic with Q4 quantization. At approximately 102 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 | 102.22 tok/s | ✅ Fits comfortably |
| Q8 | 70GB | 80GB | 71.56 tok/s | ✅ Fits comfortably |
| FP16 | 140GB | 80GB | 38.84 tok/s | ❌ Not recommended |
Need a GPU with 35GB+ VRAM? These guides match your requirements.
Check current pricing links for NVIDIA A100 80GB SXM4 and similar cards.
Open NVIDIA A100 80GB SXM4 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 →Rent cloud GPUs by the hour — no upfront hardware cost.
NVIDIA A100 80GB SXM4 can run Redhatai Llama 3.3 70B Instruct FP8 Dynamic at Q4 with an estimated 102 tok/s.
Q4 inference is estimated to need about 35GB VRAM on this page, while NVIDIA A100 80GB SXM4 has 80GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.