In early 2025, the Chinese company DeepSeek made headlines with the launch of its R1 language model, which boasts a remarkable efficiency by requiring significantly less computational power than its American counterparts. Unlike many competitors, DeepSeek has opted for transparency, publicly releasing the model weights, inference code, and detailed technical reports throughout 2024. One of the key innovations introduced by DeepSeek is a technique called multi-head latent attention, which fundamentally alters the Transformer architecture that underpins most large language models. This innovation reduces the size of a critical bottleneck known as the key-value (KV) cache by an impressive factor of 57, enabling the model to generate text over six times faster than traditional Transformers.
The video explains how language models generate text in an autoregressive manner, producing one token at a time based on previous tokens. The interactions between these tokens are managed through a mechanism called attention, which computes matrices known as attention patterns. The video compares the attention mechanisms of the GPT-2 model and DeepSeek’s R1 model, highlighting the differences in the number of attention heads and layers. While GPT-2 has 12 attention heads and 12 layers, R1 features 128 attention heads and 61 layers, resulting in a significantly larger number of attention patterns.
To understand the improvements made by DeepSeek, the video delves into the mechanics of the attention mechanism, explaining how input matrices are transformed into query and key matrices through learned weights. The attention patterns are computed by taking the dot product of these matrices, allowing the model to learn relationships between tokens. However, the computational cost of this process scales quadratically with the number of input tokens, posing a challenge for large models. The video discusses the concept of key-value caching, which allows models to store previously computed keys and values in memory, thus reducing the computational burden when generating new tokens.
DeepSeek’s innovative approach, multi-head latent attention, addresses the KV cache problem by compressing keys and values into a shared latent space. This method allows for more efficient use of memory while maintaining or even improving performance. Unlike traditional multi-query attention, which sacrifices specialization among attention heads, multi-head latent attention retains unique weights for each head, providing flexibility. The clever design enables the model to compute queries and their projections into the latent space simultaneously, significantly reducing the memory requirements for the KV cache.
Ultimately, DeepSeek’s advancements in the Transformer architecture represent a significant leap forward in the efficiency and performance of language models. By reducing the KV cache size to just 70 kilobytes per token, the R1 model not only enhances computational speed but also improves algorithmic performance. The video concludes by reflecting on the broader implications of these innovations for the future of neural networks and AI, emphasizing the importance of ongoing research and development in unlocking new capabilities for intelligent systems.
