The Kimi K3 architecture represents a scaled-up production version of the Kimi Linear model, now boasting a 2.8T parameter size, making it the largest open-weight model available. A key new component is the Latent Mo E, which compresses linear layers, similar to multi-head latent attention. The overall design trend, mirroring models like Nemotron 3 and Deep Seek V4, prioritizes inference efficiency through optimized components such as Latent Mo E and various latent attention mechanisms.

Beyond efficiency tweaks, Kimi K3 introduces attention residuals, a novel approach to enhancing the residual path by connecting layers with attention-weighted scores. This method has shown improvements in validation loss and downstream performance, with a modest increase in training and inference costs. Notably, Kimi K3 has completely replaced Ro PE layers with No PE (No Positional Embeddings) across the board, a departure from recent trends that often incorporated Ro PE in local attention layers. This adoption of No PE universally is a significant architectural choice, particularly for a frontier-level model.

Furthermore, Kimi K3 now features native multimodal support. The technical report contains additional insights into its training, but the architectural front highlights these significant advancements. This release is considered a strong overall contribution to the open-weight model landscape, building upon the foundation laid by its predecessor, Kimi Linear.