Can I Run Qwen3 14B on a NVIDIA RTX 2080 Ti?
Yes
Runs at Q4_1 — good quality with reasonable headroom.
Model size
14.8B
GPU memory
11.0GB
Smallest quant
Q2_K
Best fit
Q4_1
8 quantizations fit your 11.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q4_1BEST | 10.3 GB | 11.8 GB | 9.4 GB | +0.8 GB |
| Q4_K_M | 10.0 GB | 11.5 GB | 9.0 GB | +1.0 GB |
| Q4_K_S | 9.5 GB | 11.0 GB | 8.6 GB | +1.5 GB |
| Q4_0 | 9.3 GB | 10.8 GB | 8.5 GB | +1.7 GB |
| Q3_K_L | 7.6 GB | 9.1 GB | 7.9 GB | +3.4 GB |
| Q3_K_M | 7.2 GB | 8.7 GB | 7.3 GB | +3.8 GB |
| Q3_K_S | 6.7 GB | 8.2 GB | 6.7 GB | +4.3 GB |
| Q2_K | 5.9 GB | 7.4 GB | 5.8 GB | +5.1 GB |
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Full model details
Qwen3 14B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 2080 Ti →
Top-ranked open-source models that fit in 11.0GB.
FAQ
Can the NVIDIA RTX 2080 Ti run Qwen3 14B?
Yes. The NVIDIA RTX 2080 Ti's 11.0GB of VRAM is enough to run Qwen3 14B at Q4_1 quantization (10.3GB required).
What's the best quantization to use?
Q4_1 is the highest-precision quantization that fits in your 11.0GB. It uses about 10.3GB of memory and 11.8GB 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.