DeepSeek-V3 Explained 1: Multi-head Latent Attention

DeepSeek-V3 features major architecture innovations such as Multi-head Latent Attention (MLA), which aids in faster inference by reducing memory usage while maintaining performance. Key concepts include the Key-Value cache, Multi-Query Attention, Grouped-Query Attention, and Rotary Positional Embeddings. MLA compresses attention inputs into low-dimensional latent vectors, enhancing efficiency. The techniques ensure robust language model performance by balancing memory use and modeling capacity.