LLM Interview Series #6: What Is Grouped Query Attention?

========================================================== Preparing for AI, ML, or LLM infrastructure interviews? Practice real interview-style questions here: https://interview.vizuara.ai/ ========================================================== “What is Grouped Query Attention?” is an important LLM inference interview question because it tests whether you understand attention not just mathematically, but also from the perspective of speed, memory, and real model design. In this video, we build the idea step by step on the blackboard: Multi-head attention Multi-query attention Grouped query attention Why GQA reduces KV cache memory How GQA improves inference efficiency The tradeoffs and disadvantages of GQA Why models like Llama use grouped query attention Most candidates memorize the names: MHA, MQA, GQA. But a strong interview answer should explain what is shared, what is not shared, how query heads connect to key/value heads, and why this matters during decoding. The goal is to answer with multiple levels of depth: start from multi-head attention, motivate the memory bottleneck, introduce multi-query attention, and then show why grouped query attention is the practical middle ground. This is the kind of answer that shows clarity, depth, and genuine passion for LLM systems. ========================================================== Preparing for AI, ML, or LLM infrastructure interviews? Practice real interview-style questions here: https://interview.vizuara.ai/ ========================================================== #LLMInterview #GroupedQueryAttention #GQA #Llama #LLMInference