AllocateQwen2.5-VL GPU requirements
What it takes to run Qwen2.5-VL (72B) Instruct locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Qwen2.5-VL serves up to 32,768 tokens of context; the KV cache grows linearly toward that ceiling, so the slider below shows exactly what longer context costs in memory.
Smallest single device that fits: L40S (48 GB)
How it works
Common questions
How much VRAM does Qwen2.5-VL need?
At 8K context: roughly 161 GB at FP16, 81 GB at 8-bit, and 44 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Qwen2.5-VL?
At 4-bit, yes: a L40S (48 GB) or larger handles it at 8K context. At FP16 you need a B200 (192 GB) or larger.
Does quantization hurt Qwen2.5-VL's quality?
Modern 4-bit quantization costs a small amount of quality for a 4x memory saving; 8-bit is near-lossless. Validate on your own tasks before production.
What license is Qwen2.5-VL under?
Qwen license. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for Qwen2.5-VL?
Yes: managed inference. Allocate serves Qwen2.5-VL token-metered from $1.95 per million input tokens at provider list price, plus the 7% transaction fee. Idle time costs nothing.
More free tools
Allocate serves open-weight models like Qwen2.5-VL token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.