Can I Run Qwen3 VL 8B Instruct on a NVIDIA RTX 2080 Super?
Runs at Q5_K_M — good quality with reasonable headroom.
9 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q5_K_MBEST | 7.3 GB | 8.8 GB | 5.8 GB | +0.8 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 5.7 GB | +0.9 GB |
| Q4_1 | 6.5 GB | 8.0 GB | 5.3 GB | +1.5 GB |
| Q4_K_M | 6.3 GB | 7.8 GB | 5.0 GB | +1.7 GB |
| Q4_K_S | 6.0 GB | 7.5 GB | 4.8 GB | +2.0 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 4.8 GB | +2.0 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 4.1 GB | +3.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 3.8 GB | +3.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 3.3 GB | +4.1 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the NVIDIA RTX 2080 Super run Qwen3 VL 8B Instruct?
Yes. The NVIDIA RTX 2080 Super's 8.0GB of VRAM is enough to run Qwen3 VL 8B Instruct at Q5_K_M quantization (7.3GB required).
What's the best quantization to use?
Q5_K_M is the highest-precision quantization that fits in your 8.0GB. It uses about 7.3GB of memory and 8.8GB 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.