Can I Run Qwen3 VL 8B Thinking on a NVIDIA RTX 3080 10GB?
Runs comfortably at Q6_K — minimal quality loss.
10 quantizations fit your 10.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 8.3 GB | 9.8 GB | 6.7 GB | +1.8 GB |
| Q5_K_M | 7.3 GB | 8.8 GB | 5.8 GB | +2.8 GB |
| Q5_K_S | 7.1 GB | 8.6 GB | 5.7 GB | +2.9 GB |
| Q4_1 | 6.5 GB | 8.0 GB | 5.3 GB | +3.5 GB |
| Q4_K_M | 6.3 GB | 7.8 GB | 5.0 GB | +3.7 GB |
| Q4_K_S | 6.0 GB | 7.5 GB | 4.8 GB | +4.0 GB |
| Q4_0 | 6.0 GB | 7.5 GB | 4.8 GB | +4.0 GB |
| Q3_K_M | 4.7 GB | 6.2 GB | 4.1 GB | +5.3 GB |
| Q3_K_S | 4.4 GB | 5.9 GB | 3.8 GB | +5.6 GB |
| Q2_K | 3.9 GB | 5.4 GB | 3.3 GB | +6.1 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 10.0GB.
FAQ
Can the NVIDIA RTX 3080 10GB run Qwen3 VL 8B Thinking?
Yes. The NVIDIA RTX 3080 10GB's 10.0GB of VRAM is enough to run Qwen3 VL 8B Thinking at Q6_K quantization (8.3GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 10.0GB. It uses about 8.3GB of memory and 9.8GB 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.