How DeepSeek Rewrote the Transformer [MLA]

This episode explores the groundbreaking advancements in large language model (LLM) architecture achieved by the Chinese company DeepSeek with its R1 model. Against the backdrop of computationally expensive LLMs, DeepSeek's R1 stands out by requiring significantly less compute and by publicly releasing its model weights and technical reports. More significantly, the episode delves into DeepSeek's innovation, "multi-head latent attention," a modification to the Transformer architecture that reduces the key-value cache size by a factor of 57, resulting in over six times faster text generation. The explanation involves a detailed breakdown of the attention mechanism in LLMs, including the computation of attention patterns and the role of key-value caching. For instance, the speaker illustrates how key-value caching improves computational efficiency by storing and reusing key and value matrices. However, this caching increases memory usage, a problem DeepSeek addresses with multi-head latent attention, which allows for efficient compression and sharing of key and value information between attention heads. This ultimately leads to a significant reduction in KVCache size without sacrificing performance. What this means for the future of LLMs is a potential paradigm shift, enabling faster and more efficient models with reduced computational costs.