Qwen 2 VL 7B Instruct AWQ
Qwen 2 VL 7B Instruct is a chat model developed by Qwen, capable of handling text and image inputs with a context window of 32,768 tokens. It excels at visual understanding, achieving state-of-the-art performance on benchmarks such as DocVQA, and can also understand videos over 20 minutes long.
Input
Output
Context
33K
Max Output
33K
Parameters
8.3B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Qwen 2 VL 7B Instruct AWQ
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 Qwen 2 VL 7B Instruct AWQ
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 2 VL 7B Instruct AWQ advertises a context window of 32,768 tokens, with a maximum output of 32,768 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. Qwen 2 VL 7B Instruct AWQ is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Qwen 2 VL 7B Instruct AWQ 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 Qwen 2 VL 7B Instruct AWQ 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; Qwen 2 VL 7B Instruct AWQ 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 8.5 GB at INT8 for the weights and a default context, from the catalog's 8.3B 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: Aug 28, 2026
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