Predict, Reuse, and Repair: Accelerating Dynamic Sparse Attention for Long-Context LLM Decoding
arXiv:2606.30389v1 Announce Type: new Abstract: Dynamic sparse attention (DSA) accelerates long-context LLM decoding by attending to only the top-K KV blocks relevant to each query, but it introduces a serialized selection-to-attention dependency that emerges as a new latency bottleneck. We present PRR, a speculate-reuse-repair runtime that exploits temporal locality in DSA selections to predict likely blocks, speculate the attention over them while selection is in flight, and incrementally repair missed blocks once the true selected set is known. PRR uses a lightweight EMA-based predictor, a
The increasing demand for long-context language models is pushing the limits of current attention mechanisms, necessitating innovative solutions to decoding latency.
Accelerating LLM decoding directly impacts the commercial viability and widespread adoption of advanced AI applications, making them faster and more cost-effective.
This advancement makes long-context LLMs more practical and efficient, enabling real-time applications that were previously bottlenecked by processing speed.
- · AI software developers
- · Cloud computing providers
- · Large Language Model companies
- · Companies with inefficient LLM architectures
More efficient and faster long-context large language models become available for various applications.
This efficiency enables new classes of real-time AI applications that require processing extensive information quickly.
The reduced computational cost for long-context LLMs could lead to broader AI accessibility and novel business models.
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Read at arXiv cs.LG