Can I Run Qwen3 14B on a NVIDIA RTX A5000?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
14.8B
GPU memory
24.0GB
Smallest quant
Q2_K
Best fit
Q8_0
13 quantizations fit your 24.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 16.7 GB | 18.2 GB | 15.7 GB | +7.3 GB |
| Q6_K | 13.2 GB | 14.7 GB | 12.1 GB | +10.8 GB |
| Q5_K_M | 11.5 GB | 13.0 GB | 10.5 GB | +12.5 GB |
| Q5_K_S | 11.2 GB | 12.7 GB | 10.3 GB | +12.8 GB |
| Q5_0 | 11.2 GB | 12.7 GB | 10.3 GB | +12.8 GB |
| Q4_1 | 10.3 GB | 11.8 GB | 9.4 GB | +13.8 GB |
| Q4_K_M | 10.0 GB | 11.5 GB | 9.0 GB | +14.0 GB |
| Q4_K_S | 9.5 GB | 11.0 GB | 8.6 GB | +14.5 GB |
| Q4_0 | 9.3 GB | 10.8 GB | 8.5 GB | +14.7 GB |
| Q3_K_L | 7.6 GB | 9.1 GB | 7.9 GB | +16.4 GB |
| Q3_K_M | 7.2 GB | 8.7 GB | 7.3 GB | +16.8 GB |
| Q3_K_S | 6.7 GB | 8.2 GB | 6.7 GB | +17.3 GB |
| Q2_K | 5.9 GB | 7.4 GB | 5.8 GB | +18.1 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 A5000 →
Top-ranked open-source models that fit in 24.0GB.
FAQ
Can the NVIDIA RTX A5000 run Qwen3 14B?
Yes. The NVIDIA RTX A5000's 24.0GB of VRAM is enough to run Qwen3 14B at Q8_0 quantization (16.7GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 24.0GB. It uses about 16.7GB of memory and 18.2GB 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.