Qwen 2.5 32B
Qwen develops the Qwen 2.5 32B model, a chat-oriented large language model with a context window of 131,072 tokens, exceling in coding, mathematics, and generating long texts. Its transformer architecture incorporates RoPE, SwiGLU, RMSNorm, and Attention QKV bias, and it supports multilingual interactions in over 29 languages.
Input
Output
Context
131K
Max Output
8K
Parameters
32B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Qwen 2.5 32B
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 32B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 2.5 32B advertises a context window of 131,072 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 32B 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 2.5 32B 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.
No published benchmark result for Qwen 2.5 32B 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 2.5 32B 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 31.4 GB at INT8 for the weights and a default context, from the catalog's 32B 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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