Can I Run / Qwen3 VL 4B Instruct / on NVIDIA GTX 1660 Super
Can I Run Qwen3 VL 4B Instruct on a NVIDIA GTX 1660 Super?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
4.4B
GPU memory
6.0GB
Smallest quant
Q2_K
Best fit
Q8_0
11 quantizations fit your 6.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 5.7 GB | 7.2 GB | 4.3 GB | +0.3 GB |
| Q6_K | 4.6 GB | 6.1 GB | 3.3 GB | +1.4 GB |
| Q5_K_M | 4.1 GB | 5.6 GB | 2.9 GB | +1.9 GB |
| Q5_K_S | 4.0 GB | 5.5 GB | 2.8 GB | +2.0 GB |
| Q4_1 | 3.8 GB | 5.3 GB | 2.6 GB | +2.3 GB |
| Q4_K_M | 3.7 GB | 5.2 GB | 2.5 GB | +2.3 GB |
| Q4_K_S | 3.5 GB | 5.0 GB | 2.4 GB | +2.5 GB |
| Q4_0 | 3.5 GB | 5.0 GB | 2.4 GB | +2.5 GB |
| Q3_K_M | 2.8 GB | 4.3 GB | 2.1 GB | +3.2 GB |
| Q3_K_S | 2.7 GB | 4.2 GB | 1.9 GB | +3.3 GB |
| Q2_K | 2.5 GB | 4.0 GB | 1.7 GB | +3.5 GB |
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Full model details
Qwen3 VL 4B Instruct →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA GTX 1660 Super →
Top-ranked open-source models that fit in 6.0GB.
FAQ
Can the NVIDIA GTX 1660 Super run Qwen3 VL 4B Instruct?
Yes. The NVIDIA GTX 1660 Super's 6.0GB of VRAM is enough to run Qwen3 VL 4B Instruct at Q8_0 quantization (5.7GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 6.0GB. It uses about 5.7GB of memory and 7.2GB 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.