#Attention

Articles about Attention — exploring patterns, best practices, and real-world implementations in production systems.

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Pratik Dhanave · ·5 min read

Efficient Attention

The two costs of long context — quadratic attention compute and linear KV-cache memory — each have a family of solutions, and together they're why modern models can handle context lengths that were impossible a few years ago. Grouped-query attention shrinks the KV cache; FlashAttention computes exact attention far faster; sliding-window and sparse patterns break the quadratic. This post covers the techniques that made long context practical.

The two costs of long context each have a family of solutions, and together they're why modern models handle context lengths that were impossible a few years ago. Grouped-query attention shrinks the KV cache; FlashAttention computes exact attention far faster; sliding-window and sparse patterns break the quadratic. The techniques that made long context practical, and which cost each attacks.

#LLMs#Attention#AI Architecture#Performance#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.