Grouped-Query Attention (GQA) is a pivotal innovation in modern large language model (LLM) architecture, addressing the memory bottleneck of KV caches during autoregressive inference. Traditional multi-head attention (MHA) stores separate key-value pairs for each attention head, leading to linear memory growth with sequence length and model size. GQA reduces this by grouping query heads and sharing a single key-value head per group, dramatically cutting memory usage while preserving model quality. This article explains the motivation behind GQA, its relationship to multi-query attention (MQA), and its practical impact on inference speed and scalability. For engineers working on LLM deployment or optimization, understanding GQA is essential for building efficient systems. The post provides clear mathematical intuition and architectural diagrams, making it accessible to those familiar with transformer basics. As LLMs grow larger, techniques like GQA become critical for cost-effective serving.
A deep dive into Grouped-Query Attention (GQA), a technique that reduces KV cache memory in LLMs, enabling faster and more scalable inference.