DisCEdge manages LLM context in tokenized form replicated on edge nodes, delivering up to 14.46% faster median responses, 15% lower sync overhead, and 90% smaller client requests versus baselines while ensuring consistency.
2024.Fine-tuning LLMs for longer context and better RAG systems
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DisCEdge: Distributed Context Management for Large Language Models at the Edge
DisCEdge manages LLM context in tokenized form replicated on edge nodes, delivering up to 14.46% faster median responses, 15% lower sync overhead, and 90% smaller client requests versus baselines while ensuring consistency.