The article explores the architectural changes that enable DeepSeek's models to perform well with fewer resources, focusing on Multi-Head Latent Attention (MLA). It discusses the evolution of attention mechanisms, from Bahdanau to Transformer's Multi-Head Attention (MHA), and introduces Grouped-Query Attention (GQA) as a solution to MHA's memory inefficiencies. The article highlights DeepSeek's competitive performance despite lower reported training costs.
The article provides a detailed exploration of DeepSeek’s innovative attention mechanism, highlighting its significance in achieving state-of-the-art performance in various benchmarks. It dispels common myths about the training costs associated with DeepSeek models and emphasizes its resource efficiency compared to other large language models.
