#Long Context

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

1 post tagged with long context. ← All posts

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Pratik Dhanave · ·5 min read

The Long-Context Problem

MoE scales a model's parameters cheaply. But there's a second scaling axis that matters just as much for modern LLMs: context length — how much text the model can attend to at once. Attention's cost grows with the square of the sequence, and the memory to run it grows linearly and relentlessly, which is why long context was hard and why so much architectural ingenuity has gone into it.

MoE scales a model's parameters cheaply, but there's a second axis that matters just as much: context length. Attention's compute grows with the square of the sequence, and the KV-cache memory grows linearly and relentlessly — two distinct costs, often confused, that make long context hard. Understanding both is the setup for the efficiency techniques that solved them.

#LLMs#Long Context#AI Architecture#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.