What GPU do I need to run Qwen/Qwen2.5-VL-32B-Instruct?
33.5B parameters, published in BF16. View on Hugging Face
Set up Qwen2.5-VL-32B-InstructQwen2.5-VL-32B-Instruct is published by Qwen on Hugging Face, with 1,500,412 downloads and 499 likes to date. It's a Qwen2_5_VLForConditionalGeneration model built for image-text-to-text, 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 | 62.3 GB | 74.8 GB | A100 | 1 | $1.31/hr |
| cheaper alt. | RTX 5060 Ti | 5 | $0.550/hr | ||
| FP8 (quantized) | 31.2 GB | 37.4 GB | RTX 4090 | 1 | $0.441/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| INT4 (quantized) | 15.6 GB | 18.7 GB | RTX A5000 | 1 | $0.176/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 Qwen2.5-VL-32B-Instruct at its published (BF16) precision: 1× A100, at $1.31/hr per GPU ($1.31/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen2.5-VL-32B-Instruct: common questions
Can Qwen2.5-VL-32B-Instruct run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 74.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.31/hr.
What is the least VRAM Qwen2.5-VL-32B-Instruct can run in?
18.7 GB, at INT4 (quantized), which fits a 24 GB card, against 74.8 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 Qwen2.5-VL-32B-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.31/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen2.5 models
- Qwen2.5-VL-72B-Instruct (73.4B, BF16)
- Qwen2.5-VL-7B-Instruct (8.3B, BF16)
- NuMarkdown-8B-Thinking (8.3B, BF16)
- Qwen2.5-VL-3B-Instruct (3.8B, BF16)
- Nanonets-OCR-s (3.8B, BF16)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
