Can I Run DeepSeek-R1-Distill-Qwen-14B on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?
作者： Jakub Rusinowski · 最后更新： 2025年1月20日
Yes
Yes — DeepSeek R1 Distill Qwen 14B at Q6_K needs about 13.6 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) (~2.4 GB spare), at ~26 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~26 tok/s
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.
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RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 448 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
DeepSeek R1 on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM): memory by quantization
| 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 | ~26 tok/s | 11.5 GB |
| Q5_K_M | 12.1 GB | ✓ Yes | 16K | ~29.6 tok/s | 9.9 GB |
| Q4_K_M | 10.6 GB | ✓ Yes | 32K | ~34 tok/s | 8.5 GB |
| Q3_K_M | 8.1 GB | ✓ Yes | 32K | ~45.4 tok/s | 6 GB |
| Q2_K | 6.7 GB | ✓ Yes | 32K | ~55.7 tok/s | 4.6 GB |
Which DeepSeek R1 sizes fit
| 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 | ~34 tok/s |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | ✓ Fits | ~54.9 tok/s |
What to watch out for
- 2 larger variants of DeepSeek R1 do not fit and would need CPU offload or different hardware.
RTX 5060 Ti 16 GB desktop limitations
- The cheapest current 16 GB card, but its 448 GB/s bandwidth caps generation speed well below a 5080 on the same model.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 16 GB of VRAM on the NVIDIA GeForce RTX 5060 Ti 16GB at 448 GB/s.
- 32 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run DeepSeek R1 on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?
Yes — DeepSeek R1 Distill Qwen 14B at Q6_K needs about 13.6 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) (~2.4 GB spare), at ~26 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of DeepSeek R1 should I use on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?
Q6_K — it needs about 13.6 GB of the 16 GB available, downloads as roughly 11.5 GB, and runs at an estimated 26 tokens/sec with up to 16K of context.
What limits DeepSeek R1 on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Models on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)
- Devstral on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)
- EXAONE 3.5 on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)
- Falcon 3 on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)
- Gemma 2 Family on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)
- Gemma 3 on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)
DeepSeek R1 on GPUs
- DeepSeek R1 on NVIDIA GeForce RTX 5090
- DeepSeek R1 on NVIDIA GeForce RTX 5080
- DeepSeek R1 on NVIDIA GeForce RTX 5070 Ti
- DeepSeek R1 on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
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