What GPU do I need to run Qwen/Qwen2.5-0.5B-Instruct?
494M parameters, published in BF16. View on Hugging Face
Set up Qwen2.5-0.5B-InstructQwen2.5-0.5B-Instruct is published by Qwen on Hugging Face, with 6,008,997 downloads and 608 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 Qwen2.5-0.5B-Instruct at its published (BF16) precision: 1× A16 on vultr, at $0.059/hr per GPU ($0.059/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen2.5-0.5B-Instruct: common questions
How much VRAM does Qwen2.5-0.5B-Instruct need?
1.1 GB at BF16, 0.6 GB at FP8 (quantized), 0.3 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 0.9 GB of weights plus inference overhead is the whole requirement.
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)
