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 Q6_K needs about 13.6 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2.4 GB spare), at ~40.3 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~40.3 tok/s
| Usable memory for models | 16 GB |
| Memory bandwidth | 717 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 16 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 | ✓ Yes | 16K | ~40.3 tok/s | 11.5 GB |
| Q5_K_M | 12.1 GB | ✓ Yes | 16K | ~45.6 tok/s | 9.9 GB |
| Q4_K_M | 10.6 GB | ✓ Yes | 32K | ~52.1 tok/s | 8.5 GB |
| Q3_K_M | 8.1 GB | ✓ Yes | 32K | ~68.5 tok/s | 6 GB |
| Q2_K | 6.7 GB | ✓ Yes | 32K | ~83 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 | ~52.1 tok/s |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | ✓ Fits | ~81.8 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — DeepSeek R1 Distill Qwen 14B at Q6_K needs about 13.6 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2.4 GB spare), at ~40.3 tok/s (estimated), with room for about 16,384 tokens of context.
Q6_K — it needs about 13.6 GB of the 16 GB available, downloads as roughly 11.5 GB, and runs at an estimated 40.3 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
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