22,580: GPT-2 to Kimi K3, explained

Over seven years, the Kimi K3 model has evolved significantly from its predecessor, GPT-2, through substantial architectural advancements, increasing its scale by a factor of 22,580. While GPT-2 utilized a decoder-only architecture requiring repetitive computations for each token, Kimi K3 introduces innovations like the KV cache and linear attention to optimize memory usage and computational efficiency. Moreover, the Kimi K3 employs a blend of Kimi Delta Attention and Multi-head Latent Attention (MLA) to enhance memory retention, alongside a Mixture-of-Experts layer to manage capacity more effectively. The key improvements in Kimi K3 include the implementation of Gated DeltaNet, which combines adaptive memory management with precise key-value association learning, and the use of selective retrieval mechanisms such as Attention Residuals (AttnRes) to mitigate hidden-state growth. These enhancements allow the model to allocate capacity purposefully, reducing memory traffic and improving overall performance without relying solely on scaling up parameter count.

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