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Akashic: Efficient LLM Inference with MemAttention for Long Contexts
Akashic is a low-overhead LLM inference service designed to handle long multi-turn interactions by avoiding full history replay. It uses MemAttention to improve serving efficiency and output quality by focusing on task-relevant context.
Long context accumulation in LLM agents increases computational cost and can degrade output quality by mixing relevant and irrelevant information. Akashic addresses these challenges, enabling more efficient and effective LLM inference in complex workflows.
AI-assisted summary based on listed sources.
Score 75
Source Type arxiv
Reposts 0
Topic Quality 58
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