Can I Run QwQ 32B on a NVIDIA DGX Spark?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
32.8B
GPU memory
128GB
Smallest quant
Q2_K
Best fit
Q8_0
11 quantizations fit your 128GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 35.9 GB | 37.4 GB | 34.8 GB | +92.2 GB |
| Q6_K | 28.0 GB | 29.5 GB | 26.9 GB | +100.0 GB |
| Q5_K_M | 24.3 GB | 25.8 GB | 23.3 GB | +103.7 GB |
| Q5_K_S | 23.6 GB | 25.1 GB | 22.6 GB | +104.4 GB |
| Q5_0 | 23.6 GB | 25.1 GB | 22.6 GB | +104.5 GB |
| Q4_K_M | 20.9 GB | 22.4 GB | 19.9 GB | +107.1 GB |
| Q4_K_S | 19.8 GB | 21.3 GB | 18.8 GB | +108.2 GB |
| Q3_K_L | 15.6 GB | 17.1 GB | 17.3 GB | +112.4 GB |
| Q3_K_M | 14.7 GB | 16.2 GB | 15.9 GB | +113.3 GB |
| Q3_K_S | 13.6 GB | 15.1 GB | 14.4 GB | +114.4 GB |
| Q2_K | 11.8 GB | 13.3 GB | 12.3 GB | +116.2 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 DGX Spark →
Top-ranked open-source models that fit in 128GB.
FAQ
Can the NVIDIA DGX Spark run QwQ 32B?
Yes. The NVIDIA DGX Spark's 128GB of unified memory is enough to run QwQ 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 128GB. 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.