Hybrid Attention Explained: How Transformers Handle Long Context Efficiently

Hybrid Attention is a key technique used to make Transformers more efficient for long-context tasks. Instead of using full attention everywhere, hybrid attention combines local, global, and sparse attention patterns to reduce computation while preserving performance. In this video, we break down how hybrid attention works and why it’s essential for scaling modern large language models. If you're studying Transformers, LLM optimization, or AI systems engineering, this video will give you a clear understanding of how attention mechanisms scale. #HybridAttention #Transformers #AttentionMechani…