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Kimi K3 Architecture Notes
The newly released open-weight Kimi K3 model shifts toward improved inference efficiency and native multimodal capabilities compared to its predecessor, Kimi Linear.
What: Kimi K3 adopts a scaled-up production architecture, focusing on optimizing token processing speed and integrating visual data processing directly into the model.
Why it matters: The focus on inference efficiency suggests a broader trend where labs are prioritizing lower operational costs for large models over raw capacity increases.
Decoder
- Inference: The process of using a trained model to make predictions on new data.
- Multimodal: The ability of a model to process and understand multiple types of data inputs, such as text and images.
Original article
Kimi K3's architecture is essentially a scaled-up production version of the Kimi Linear model released last year. The model seems to be trending toward better inference efficiency. It now has native multimodal support. This post summarizes some of the major changes and additions noted in the open-weight model release.
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