GPTs Had to Fix Attention

As Large Language Models scale to longer contexts and more attention heads, one hidden bottleneck starts to dominate: memory.

Every attention head stores its own keys and values, and during inference, that data grows rapidly with the sequence length. Without optimization, long conversations would quickly become impractical.

In this video, we explore Grouped Query Attention (GQA), a simple but powerful optimization used in modern models like LLaMA 2 and Mistral to dramatically reduce attention memory usage without sacrificing performance.

You’ll learn:

- Why multi-head attention becomes expensive at scale
- The relationship between attention heads and KV Cache memory
- How Multi-Query Attention (MQA) first reduced memory cost
- Why MQA sometimes hurts model quality
- How Grouped Query Attention (GQA) strikes the balance
- How query heads share key–value groups during inference
- Why GQA enables faster and longer-context LLMs

Grouped Query Attention is one of the key architectural optimizations that makes modern large-scale models practical to deploy.

Видео GPTs Had to Fix Attention канала ML Guy