Can I Run Qwen3 32B on a NVIDIA RTX 5000 Ada?
Yes
Runs comfortably at Q6_K — minimal quality loss.
Model size
32.8B
GPU memory
32.0GB
Smallest quant
Q2_K
Best fit
Q6_K
12 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 28.0 GB | 29.5 GB | 26.9 GB | +4.0 GB |
| Q5_K_M | 24.3 GB | 25.8 GB | 23.2 GB | +7.7 GB |
| Q5_K_S | 23.6 GB | 25.1 GB | 22.6 GB | +8.4 GB |
| Q5_0 | 23.6 GB | 25.1 GB | 22.6 GB | +8.4 GB |
| Q4_1 | 21.5 GB | 23.0 GB | 20.6 GB | +10.5 GB |
| Q4_K_M | 20.9 GB | 22.4 GB | 19.8 GB | +11.1 GB |
| Q4_K_S | 19.8 GB | 21.3 GB | 18.8 GB | +12.2 GB |
| Q4_0 | 19.4 GB | 20.9 GB | 18.7 GB | +12.6 GB |
| Q3_K_L | 15.6 GB | 17.1 GB | 17.3 GB | +16.4 GB |
| Q3_K_M | 14.7 GB | 16.2 GB | 16.0 GB | +17.3 GB |
| Q3_K_S | 13.6 GB | 15.1 GB | 14.4 GB | +18.4 GB |
| Q2_K | 11.8 GB | 13.3 GB | 12.3 GB | +20.2 GB |
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Full model details
Qwen3 32B →
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 32B?
Yes. The NVIDIA RTX 5000 Ada's 32.0GB of VRAM is enough to run Qwen3 32B at Q6_K quantization (28.0GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 32.0GB. It uses about 28.0GB of memory and 29.5GB 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.