This page answers Deepseek AI Deepseek Coder 33B Instruct q4 quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Deepseek AI Deepseek Coder 33B Instruct typically needs around 17GB VRAM at Q4, and 21GB 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 | 334 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q4 | 302 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q4 | 217 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q4 | 209 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q4 | 137 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q4 | 131 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q4 | 128 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q4 | 104 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q4 | 99 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q4 | 79 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q4 | 78 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q4 | 72 tok/s | View full compatibility | Buy options |
Deepseek AI Deepseek Coder 33B Instruct at Q4 is estimated to require about 17GB VRAM minimum, with 21GB 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 17GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.