What GPU do I need to run Qwen/Qwen2.5-3B-Instruct?
3.1B parameters, published in BF16. View on Hugging Face
Set up Qwen2.5-3B-InstructQwen2.5-3B-Instruct is published by Qwen on Hugging Face, with 7,635,044 downloads and 557 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 Qwen2.5-3B-Instruct at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen2.5-3B-Instruct: common questions
Does Qwen2.5-3B-Instruct fit on a 8 GB GPU?
Yes. At BF16 it needs 6.9 GB of VRAM, so an 8 GB card holds it with 1.1 GB to spare. A 6 GB card is not enough for it at BF16.
What is the least VRAM Qwen2.5-3B-Instruct can run in?
1.7 GB, at INT4 (quantized), which fits a 6 GB card, against 6.9 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.
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)
