arXiv:2606.00024v1 Announce Type: new Abstract: Long-context decoding in Large Language Models (LLMs) is severely constrained by the memory bandwidth required to fetch the extensive Key-Value (KV) cache. Most existing KV management methods rely on key-only pruning before decoding, despite the evidence that attention outputs depend jointly on keys and values, as incorporating values in their methods incurs prohibitive additional overhead. In this paper, we propose Attention Run-time Termination (ART), a lightweight run-time mechanism that tracks accumulated attention outputs during kernel execu
The increasing scale and complexity of Large Language Models (LLMs) are pushing existing memory bandwidth and decoding efficiency to their limits, necessitating innovative solutions.
Improved decoding efficiency for LLMs, especially in long-context scenarios, directly impacts the cost, speed, and capability of leading-edge AI applications and infrastructure.
This research suggests a more efficient way to manage LLM decoding, potentially lowering operating costs and enabling longer context windows for AI models.
- · AI compute providers
- · Large Language Model developers
- · Enterprises leveraging LLMs for long-context tasks
- · AI infrastructure companies
- · Less efficient LLM decoding methods
- · Companies with high LLM inference costs
Reduced computational overhead for deploying large language models with extensive context capabilities.
Accelerated development and adoption of LLMs in applications requiring deep contextual understanding, making AI more accessible and powerful.
Enhanced competition in the AI services market due to lower inference costs, potentially driving further innovation and broader AI integration across industries.
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