This page answers Deepseek AI Deepseek Math V2 q4 quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Deepseek AI Deepseek Math V2 typically needs around 1GB VRAM at Q4, and 2GB is safer for smoother usage.
Exact Q4 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 | Q4 | 1,145 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q4 | 1,034 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q4 | 743 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q4 | 716 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q4 | 471 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q4 | 450 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q4 | 438 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q4 | 356 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q4 | 341 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q4 | 270 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q4 | 268 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q4 | 248 tok/s | View full compatibility | Buy options |
Deepseek AI Deepseek Math V2 at Q4 is estimated to require about 1GB VRAM minimum, with 2GB 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.
Q4 is a balance point between memory usage and quality. If your GPU is below 1GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.