This page answers Deepseek AI Deepseek R1 fp16 queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Deepseek AI Deepseek R1 typically needs around 14GB VRAM at FP16, and 17GB is safer for smoother usage.
Exact FP16 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 | FP16 | 363 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | FP16 | 327 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | FP16 | 235 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | FP16 | 227 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | FP16 | 149 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | FP16 | 142 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | FP16 | 139 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | FP16 | 113 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | FP16 | 108 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | FP16 | 86 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | FP16 | 85 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | FP16 | 79 tok/s | View full compatibility | Buy options |
Deepseek AI Deepseek R1 at FP16 is estimated to require about 14GB VRAM minimum, with 17GB 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.
FP16 is a balance point between memory usage and quality. If your GPU is below 14GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.