Can I Run QwQ 32B on a NVIDIA DGX Spark?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
32.0B
GPU memory
128GB
Smallest quant
Q4_K_M
Best fit
fp16
5 quantizations fit your 128GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 65.0 GB | 66.5 GB | 64.0 GB | +63.0 GB |
| Q8_0 | 35.0 GB | 36.5 GB | 34.0 GB | +93.0 GB |
| Q6_K | 27.4 GB | 28.9 GB | 26.4 GB | +100.6 GB |
| Q5_K_M | 23.7 GB | 25.2 GB | 22.7 GB | +104.3 GB |
| Q4_K_M | 20.4 GB | 21.9 GB | 19.4 GB | +107.6 GB |
Try it in the cloud first
Don't want to download QwQ 32B just to try it? Use a hosted API or rent a GPU by the second.
Affiliate links — we earn a commission at no cost to you.
Advertisement
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 fp16 quantization (65.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 128GB. It uses about 65.0GB of memory and 66.5GB 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.