arXiv:2606.04511v1 Announce Type: cross Abstract: Sparse attention reduces compute and memory bandwidth for long-context LLM inference. However, two key challenges remain: (1) KV cache capacity still grows with sequence length, and offloading to CPU memory introduces a PCIe transfer bottleneck; (2) the sparse selection step itself retains $O(T^2)$ complexity and can dominate attention cost at long contexts. We propose SparDA, a decoupled sparse attention architecture that introduces a fourth per-layer projection, the Forecast, alongside Query, Key, and Value. The Forecast predicts the KV block
The continuous drive for more performant and efficient large language models necessitates innovations in attention mechanisms to handle increasingly long contexts without prohibitive computational costs.
Efficient long-context LLM inference democratizes access to advanced AI capabilities and reduces the infrastructure burden for AI developers and users, potentially accelerating AI adoption and applications.
This innovation improves the efficiency and scalability of large language models, enabling them to process longer sequences of information with reduced computational and memory overhead.
- · AI developers
- · Cloud computing providers
- · Hyperscalers
- · Companies with inefficient LLM architectures
General-purpose LLMs become more capable of processing and generating human-like text over extended conversations or documents.
New AI applications emerge that rely on understanding very long-form content, such as advanced summarization, comprehensive legal analysis, or complex scientific research.
The reduced cost of inference for long contexts contributes to a lower barrier to entry for AI innovation, fostering a more competitive and diverse AI ecosystem.
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Read at arXiv cs.LG