BAAIInfinity Instruct 3M 0625 Yi 1.5 9B
Infinity Instruct 3M 0625 Yi 1.5 9B is an open-source chat model developed by BAAI (Beijing Academy of Artificial Intelligence). It is an instruction-tuned version of the Yi-1.5-9B base model, trained on the Infinity-Instruct dataset without reinforcement learning from human feedback (RLHF).
Input
Output
Context
4K
Max Output
-
Parameters
8.8B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Infinity Instruct 3M 0625 Yi 1.5 9B
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 Infinity Instruct 3M 0625 Yi 1.5 9B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Infinity Instruct 3M 0625 Yi 1.5 9B advertises a context window of 4,096 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Infinity Instruct 3M 0625 Yi 1.5 9B 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.
Infinity Instruct 3M 0625 Yi 1.5 9B 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 Infinity Instruct 3M 0625 Yi 1.5 9B 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; Infinity Instruct 3M 0625 Yi 1.5 9B 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 9.0 GB at INT8 for the weights and a default context, from the catalog's 8.8B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 7, 2026
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