Can I Run QwQ 32B on a NVIDIA RTX 5090?
Yes
Runs comfortably at Q6_K — minimal quality loss.
Model size
32.0B
GPU memory
32.0GB
Smallest quant
Q4_K_M
Best fit
Q6_K
3 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 27.4 GB | 28.9 GB | 26.4 GB | +4.6 GB |
| Q5_K_M | 23.7 GB | 25.2 GB | 22.7 GB | +8.3 GB |
| Q4_K_M | 20.4 GB | 21.9 GB | 19.4 GB | +11.6 GB |
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Full model details
QwQ 32B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 5090 →
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the NVIDIA RTX 5090 run QwQ 32B?
Yes. The NVIDIA RTX 5090's 32.0GB of VRAM is enough to run QwQ 32B at Q6_K quantization (27.4GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 32.0GB. It uses about 27.4GB of memory and 28.9GB 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.