What GPU do I need to run Qwen/Qwen2.5-VL-3B-Instruct?
3.8B parameters, published in BF16. View on Hugging Face
Qwen2.5-VL-3B-Instruct is published by Qwen on Hugging Face, with 6,211,749 downloads and 688 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.
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 Qwen2.5-VL-3B-Instruct at its published (BF16) precision: 1× RTX 3060 on simplepod, at $0.070/hr per GPU ($0.070/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)
- Qwen3.5-9B (9.7B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen2.5-7B-Instruct (7.6B, BF16)
- Qwen2.5-1.5B-Instruct (1.5B, BF16)
