Can I Run Qwen3 14B on a NVIDIA RTX 5000 Ada?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
14.8B
GPU memory
32.0GB
Smallest quant
Q2_K
Best fit
fp16
14 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 30.6 GB | 32.1 GB | 29.5 GB | +1.4 GB |
| Q8_0 | 16.7 GB | 18.2 GB | 15.7 GB | +15.3 GB |
| Q6_K | 13.2 GB | 14.7 GB | 12.1 GB | +18.8 GB |
| Q5_K_M | 11.5 GB | 13.0 GB | 10.5 GB | +20.5 GB |
| Q5_K_S | 11.2 GB | 12.7 GB | 10.3 GB | +20.8 GB |
| Q5_0 | 11.2 GB | 12.7 GB | 10.3 GB | +20.8 GB |
| Q4_1 | 10.3 GB | 11.8 GB | 9.4 GB | +21.8 GB |
| Q4_K_M | 10.0 GB | 11.5 GB | 9.0 GB | +22.0 GB |
| Q4_K_S | 9.5 GB | 11.0 GB | 8.6 GB | +22.5 GB |
| Q4_0 | 9.3 GB | 10.8 GB | 8.5 GB | +22.7 GB |
| Q3_K_L | 7.6 GB | 9.1 GB | 7.9 GB | +24.4 GB |
| Q3_K_M | 7.2 GB | 8.7 GB | 7.3 GB | +24.8 GB |
| Q3_K_S | 6.7 GB | 8.2 GB | 6.7 GB | +25.3 GB |
| Q2_K | 5.9 GB | 7.4 GB | 5.8 GB | +26.1 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 5000 Ada →
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the NVIDIA RTX 5000 Ada run Qwen3 14B?
Yes. The NVIDIA RTX 5000 Ada's 32.0GB of VRAM is enough to run Qwen3 14B at fp16 quantization (30.6GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 32.0GB. It uses about 30.6GB of memory and 32.1GB 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.