What GPU do I need to run Qwen/Qwen-72B?
72.3B parameters, published in BF16. View on Hugging Face
Set up Qwen-72BQwen-72B is published by Qwen on Hugging Face, with 3,244,398 downloads and 361 likes to date. It's a QWenLMHeadModel 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 134.6 GB | 161.6 GB | RTX A5000 | 7 | $1.23/hr |
| FP8 (quantized) | 67.3 GB | 80.8 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 6 | $0.660/hr | ||
| INT4 (quantized) | 33.7 GB | 40.4 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr |
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 Qwen-72B at its published (BF16) precision: 7× RTX A5000, at $0.176/hr per GPU ($1.23/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen-72B: common questions
Can Qwen-72B run on a single GPU?
No. At BF16 it needs 161.6 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 24.0 GB RTX A5000, and it takes 7 of them.
How many GPUs do I need to run Qwen-72B?
7 at BF16. It needs 161.6 GB of VRAM and the cheapest capable live offer is a 24.0 GB RTX A5000, so 7 of them come to $1.23/hr in total.
Does quantizing Qwen-72B lower the GPU bill?
Yes. At BF16 the cheapest live fit is 7 RTX A5000 cards at $1.23/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/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
- Qwen-7B-Chat (7.7B, BF16)
- Qwen-7B (7.7B, BF16)
- nb-asr-beta-qwen06b-lunde05 (782M, BF16)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
