Qwen 2.5 72B Instruct AWQ
Qwen2.5 72B Instruct is a chat model developed by Qwen, capable of processing up to 32,768 tokens and handling streaming and structured output. It is licensed under an open-source license, allowing for flexibility in its use and modification.
Input
Output
Context
33K
Max Output
8K
Parameters
73B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Resources & Links
Estimated GPU requirements for Qwen 2.5 72B 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 2.5 72B Instruct AWQ
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 2.5 72B Instruct AWQ advertises a context window of 32,768 tokens, with a maximum output of 8,192 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 2.5 72B Instruct AWQ is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
Qwen 2.5 72B Instruct AWQ accepts Text 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.
Qwen 2.5 72B Instruct AWQ has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Qwen 2.5 72B Instruct AWQ is shown as not available rather than estimated.
Yes. Qwen 2.5 72B Instruct AWQ is served on the managed pool through the OpenAI-compatible endpoint as Qwen/Qwen2.5-72B-Instruct, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 71.3 GB at INT8 for the weights and a default context, from the catalog's 73B 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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