Can I Run DeepSeek R1 Distill Qwen 1.5B on a NVIDIA GTX 1650?
Runs at Q8_0 — near-lossless quality.
12 quantizations fit your 4.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 2.9 GB | 4.4 GB | 1.9 GB | +1.1 GB |
| Q6_K | 2.5 GB | 4.0 GB | 1.5 GB | +1.5 GB |
| Q5_K_M | 2.3 GB | 3.8 GB | 1.3 GB | +1.7 GB |
| Q5_K_S | 2.2 GB | 3.7 GB | 1.3 GB | +1.8 GB |
| Q4_1 | 2.1 GB | 3.6 GB | 1.2 GB | +1.9 GB |
| Q4_K_M | 2.1 GB | 3.6 GB | 1.1 GB | +1.9 GB |
| Q4_K_S | 2.0 GB | 3.5 GB | 1.1 GB | +2.0 GB |
| Q4_0 | 2.0 GB | 3.5 GB | 1.1 GB | +2.0 GB |
| Q3_K_L | 1.8 GB | 3.3 GB | 1.0 GB | +2.2 GB |
| Q3_K_M | 1.8 GB | 3.3 GB | 0.9 GB | +2.3 GB |
| Q3_K_S | 1.7 GB | 3.2 GB | 0.9 GB | +2.3 GB |
| Q2_K | 1.6 GB | 3.1 GB | 0.8 GB | +2.4 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 4.0GB.
FAQ
Can the NVIDIA GTX 1650 run DeepSeek R1 Distill Qwen 1.5B?
Yes. The NVIDIA GTX 1650's 4.0GB of VRAM is enough to run DeepSeek R1 Distill Qwen 1.5B at Q8_0 quantization (2.9GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 4.0GB. It uses about 2.9GB of memory and 4.4GB 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.