Can I Run Qwen3 32B on a NVIDIA RTX A6000?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
32.8B
GPU memory
48.0GB
Smallest quant
Q2_K
Best fit
Q8_0
13 quantizations fit your 48.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 35.9 GB | 37.4 GB | 34.8 GB | +12.1 GB |
| Q6_K | 28.0 GB | 29.5 GB | 26.9 GB | +20.0 GB |
| Q5_K_M | 24.3 GB | 25.8 GB | 23.2 GB | +23.7 GB |
| Q5_K_S | 23.6 GB | 25.1 GB | 22.6 GB | +24.4 GB |
| Q5_0 | 23.6 GB | 25.1 GB | 22.6 GB | +24.4 GB |
| Q4_1 | 21.5 GB | 23.0 GB | 20.6 GB | +26.5 GB |
| Q4_K_M | 20.9 GB | 22.4 GB | 19.8 GB | +27.1 GB |
| Q4_K_S | 19.8 GB | 21.3 GB | 18.8 GB | +28.2 GB |
| Q4_0 | 19.4 GB | 20.9 GB | 18.7 GB | +28.6 GB |
| Q3_K_L | 15.6 GB | 17.1 GB | 17.3 GB | +32.4 GB |
| Q3_K_M | 14.7 GB | 16.2 GB | 16.0 GB | +33.3 GB |
| Q3_K_S | 13.6 GB | 15.1 GB | 14.4 GB | +34.4 GB |
| Q2_K | 11.8 GB | 13.3 GB | 12.3 GB | +36.2 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 A6000 →
Top-ranked open-source models that fit in 48.0GB.
FAQ
Can the NVIDIA RTX A6000 run Qwen3 32B?
Yes. The NVIDIA RTX A6000's 48.0GB of VRAM is enough to run Qwen3 32B at Q8_0 quantization (35.9GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 48.0GB. It uses about 35.9GB of memory and 37.4GB 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.