Can I Run DeepSeek R1 Distill Qwen 1.5B on a NVIDIA RTX 5060?
Runs at full precision (fp16). Zero quality loss.
13 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 4.6 GB | 6.1 GB | 3.6 GB | +3.4 GB |
| Q8_0 | 2.9 GB | 4.4 GB | 1.9 GB | +5.1 GB |
| Q6_K | 2.5 GB | 4.0 GB | 1.5 GB | +5.5 GB |
| Q5_K_M | 2.3 GB | 3.8 GB | 1.3 GB | +5.7 GB |
| Q5_K_S | 2.2 GB | 3.7 GB | 1.3 GB | +5.8 GB |
| Q4_1 | 2.1 GB | 3.6 GB | 1.2 GB | +5.9 GB |
| Q4_K_M | 2.1 GB | 3.6 GB | 1.1 GB | +5.9 GB |
| Q4_K_S | 2.0 GB | 3.5 GB | 1.1 GB | +6.0 GB |
| Q4_0 | 2.0 GB | 3.5 GB | 1.1 GB | +6.0 GB |
| Q3_K_L | 1.8 GB | 3.3 GB | 1.0 GB | +6.2 GB |
| Q3_K_M | 1.8 GB | 3.3 GB | 0.9 GB | +6.3 GB |
| Q3_K_S | 1.7 GB | 3.2 GB | 0.9 GB | +6.3 GB |
| Q2_K | 1.6 GB | 3.1 GB | 0.8 GB | +6.4 GB |
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Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the NVIDIA RTX 5060 run DeepSeek R1 Distill Qwen 1.5B?
Yes. The NVIDIA RTX 5060's 8.0GB of VRAM is enough to run DeepSeek R1 Distill Qwen 1.5B at fp16 quantization (4.6GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 8.0GB. It uses about 4.6GB of memory and 6.1GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.