DeepSeek's V4 model card has confirmed three core components of its architecture, following the open-sourcing of the TileKernels library. The model employs Manifold-Constrained Hyper-Connections (mHC), a mixture of experts (MoE) architecture with Top-k expert routing, and FP4+FP8 mixed precision for weight storage. These elements were accurately inferred from the TileKernels library. However, the model card does not mention the Engram conditional memory module, which was previously speculated but remains unconfirmed. Additionally, the card introduces new features not covered by TileKernels, including a hybrid attention mechanism (CSA + HCA) that significantly enhances long-context efficiency, reducing inference FLOPs to 27% and KV cache to 10% of V3.2's under 1M context. The training process now utilizes the Muon optimizer.