This page answers Deepseek AI Deepseek V3 0324 q5_k_m quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Deepseek AI Deepseek V3 0324 typically needs around 24GB VRAM at Q5_K_M, and 29GB is safer for smoother usage.
Estimated from Q4 and Q8 requirement bounds using midpoint interpolation.
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 V3 0324 at Q5_K_M is estimated to require about 24GB VRAM minimum, with 29GB 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.
Q5_K_M is a balance point between memory usage and quality. If your GPU is below 24GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.