Written by Jakub Rusinowski · Last updated January 20, 2025
These figures are for DeepSeek-R1-Distill-Qwen-14B, a distill of Qwen2.5-14B — not the full DeepSeek R1. The full DeepSeek R1 (671B) needs about 405 GB of weights at Q4_K_M and is a different model.
Yes
Yes — DeepSeek R1 Distill Qwen 14B at Q4_K_M needs about 10.6 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.4 GB spare), at ~27.7 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q4_K_M · Estimated speed: ~27.7 tok/s
| Usable memory for models | 12 GB |
| Memory bandwidth | 360 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 30.1 GB | ✗ No | — | — | 28 GB |
| Q8_0 | 17 GB | ✗ No | — | — | 14.9 GB |
| Q6_K | 13.6 GB | ✗ No | — | — | 11.5 GB |
| Q5_K_M | 12.1 GB | ✗ No | — | — | 9.9 GB |
| Q4_K_M | 10.6 GB | ✓ Yes | 16K | ~27.7 tok/s | 8.5 GB |
| Q3_K_M | 8.1 GB | ✓ Yes | 16K | ~37.3 tok/s | 6 GB |
| Q2_K | 6.7 GB | ✓ Yes | 32K | ~45.9 tok/s | 4.6 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| DeepSeek R1 (671B) | 406.5 GB | ✗ Too large | — |
| DeepSeek R1 Distill Qwen 32B | 22.3 GB | ✗ Too large | — |
| DeepSeek R1 Distill Qwen 14B | 10.6 GB | ✓ Fits | ~27.7 tok/s |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | ✓ Fits | ~45.2 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — DeepSeek R1 Distill Qwen 14B at Q4_K_M needs about 10.6 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.4 GB spare), at ~27.7 tok/s (estimated), with room for about 16,384 tokens of context.
Q4_K_M — it needs about 10.6 GB of the 12 GB available, downloads as roughly 8.5 GB, and runs at an estimated 27.7 tokens/sec with up to 16K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
← Can I Run It? | DeepSeek R1 model page | Check your hardware