This page answers Deepseek AI Deepseek Ocr 2 q8 quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Deepseek AI Deepseek Ocr 2 typically needs around 2GB VRAM at Q8, and 3GB is safer for smoother usage.
Exact Q8 requirement from model requirement data.
Throughput data below uses available compatibility measurements/estimates and is sorted by tokens per second for this model.
Need general guidance? Review full methodology.
| GPU | VRAM | Quantization | Speed | Compatibility | Buy |
|---|---|---|---|---|---|
| AMD Instinct MI300X | 192GB | Q8 | 801 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q8 | 724 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q8 | 520 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q8 | 501 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q8 | 330 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q8 | 315 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q8 | 307 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q8 | 250 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q8 | 239 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q8 | 189 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q8 | 187 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q8 | 174 tok/s | View full compatibility | Buy options |
Deepseek AI Deepseek Ocr 2 at Q8 is estimated to require about 2GB VRAM minimum, with 3GB recommended for smoother operation.
Start with AMD Instinct MI300X, NVIDIA H200 SXM 141GB, NVIDIA H100 SXM5 80GB and review each compatibility page for full speed and fit details.
Q8 is a balance point between memory usage and quality. If your GPU is below 2GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.