What GPU do I need to run Qwen/QwQ-32B?
32.8B parameters, published in BF16. View on Hugging Face
QwQ-32B is published by Qwen on Hugging Face, with 377,522 downloads and 2,955 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 caveat: 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.
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)
- Qwen2.5-1.5B-Instruct (1.5B, BF16)
- Qwen2.5-7B-Instruct (7.6B, BF16)
- Qwen3-Embedding-0.6B (596M, BF16)
- Qwen3-32B (32.8B, BF16)
