Can I Run Qwen3 14B on a NVIDIA RTX 3080 10GB?
Runs at Q4_K_M — good quality with reasonable headroom.
7 quantizations fit your 10.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_K_MBEST | 10.0 GB | 11.5 GB | 9.0 GB | +0.0 GB |
| Q4_K_S | 9.5 GB | 11.0 GB | 8.6 GB | +0.5 GB |
| Q4_0 | 9.3 GB | 10.8 GB | 8.5 GB | +0.7 GB |
| Q3_K_L | 7.6 GB | 9.1 GB | 7.9 GB | +2.4 GB |
| Q3_K_M | 7.2 GB | 8.7 GB | 7.3 GB | +2.8 GB |
| Q3_K_S | 6.7 GB | 8.2 GB | 6.7 GB | +3.3 GB |
| Q2_K | 5.9 GB | 7.4 GB | 5.8 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 10.0GB.
FAQ
Can the NVIDIA RTX 3080 10GB run Qwen3 14B?
Yes. The NVIDIA RTX 3080 10GB's 10.0GB of VRAM is enough to run Qwen3 14B at Q4_K_M quantization (10.0GB required).
What's the best quantization to use?
Q4_K_M is the highest-precision quantization that fits in your 10.0GB. It uses about 10.0GB of memory and 11.5GB 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.