Kimi K2 Explained: The 1 Trillion Parameter Model

This post introduces Kimi K2, a 1 trillion parameter Mixture-of-Experts (MoE) model from Moonshot Labs, building on the DeepSeek architecture. Key technical innovations include a significantly increased number of specialized experts (384) with fewer attention heads for improved focus, a post-training approach using synthetic agentic data from simulated tool interactions, and the MuonClip optimizer which clips attention logits to prevent training instability and loss spikes. The post also details the production deployment challenges for such a large model, including tensor parallelism, KV-cache optimization, and load balancing, and highlights Baseten's infrastructure for making Kimi K2 accessible with low latency.