Can I Run DeepSeek R1 0528 Qwen3 8B on a NVIDIA GTX 1660 Ti?
Runs at Q4_K_M — good quality with reasonable headroom.
7 quantizations fit your 6.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_K_MBEST | 6.0 GB | 7.5 GB | 5.0 GB | +0.0 GB |
| Q4_K_S | 5.7 GB | 7.2 GB | 4.8 GB | +0.3 GB |
| Q4_0 | 5.6 GB | 7.1 GB | 4.8 GB | +0.4 GB |
| Q3_K_L | 4.7 GB | 6.2 GB | 4.4 GB | +1.3 GB |
| Q3_K_M | 4.4 GB | 5.9 GB | 4.1 GB | +1.6 GB |
| Q3_K_S | 4.2 GB | 5.7 GB | 3.8 GB | +1.8 GB |
| Q2_K | 3.7 GB | 5.2 GB | 3.3 GB | +2.3 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 6.0GB.
FAQ
Can the NVIDIA GTX 1660 Ti run DeepSeek R1 0528 Qwen3 8B?
Yes. The NVIDIA GTX 1660 Ti's 6.0GB of VRAM is enough to run DeepSeek R1 0528 Qwen3 8B at Q4_K_M quantization (6.0GB required).
What's the best quantization to use?
Q4_K_M is the highest-precision quantization that fits in your 6.0GB. It uses about 6.0GB of memory and 7.5GB 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.