qwen2-vl-72b-instruct
Qwen builds the Qwen2-VL (72B) Instruct model, a vision model capable of processing text and image inputs with a context window of 32,768 tokens. It is genuinely best at handling tasks that require extended thinking and generating structured output, making it suitable for complex applications.
Input
Output
Context
33K
Max Output
2K
Parameters
72B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for qwen2-vl-72b-instruct
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about qwen2-vl-72b-instruct
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
qwen2-vl-72b-instruct advertises a context window of 32,768 tokens, with a maximum output of 2,000 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. qwen2-vl-72b-instruct is an open-weight model released under the Tongyi qianwen license, so the weights can be downloaded and self-hosted within that license's terms.
qwen2-vl-72b-instruct accepts Text and Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for qwen2-vl-72b-instruct is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; qwen2-vl-72b-instruct is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 70.3 GB at INT8 for the weights and a default context, from the catalog's 72B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Sep 13, 2026
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