What GPU do I need to run Qwen/QwQ-32B?
32.8B parameters, published in BF16. View on Hugging Face
Set up QwQ-32BQwQ-32B is published by Qwen on Hugging Face, with 59,291 downloads and 2,963 likes to date. It's a Qwen2ForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run QwQ-32B at its published (BF16) precision: 1× A100 on runpod, at $1.19/hr per GPU ($1.19/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
QwQ-32B: common questions
Can QwQ-32B run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 73.2 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 on runpod at $1.19/hr.
What is the least VRAM QwQ-32B can run in?
18.3 GB, at INT4 (quantized), which fits a 24 GB card, against 73.2 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing QwQ-32B lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 on runpod at $1.19/hr. At INT4 (quantized) it drops to one RTX A5000 on runpod at $0.160/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen models
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
- Qwen2.5-7B-Instruct (7.6B, BF16)
- Qwen3-VL-8B-Instruct (8.8B, BF16)
