For machine learning engineers deploying LLMs at scale, the equation is familiar and unforgiving: as context length increases, attention computation costs...

For machine learning engineers deploying LLMs at scale, the equation is familiar and unforgiving: as context length increases, attention computation costs explode. Whether you’re dealing with retrieval-augmented generation (RAG) pipelines, agentic AI workflows, or long-form content generation, the complexity of attention remains a primary bottleneck. This post explains a technique known as…

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