MSAO cuts end-to-end latency by 30% and resource overhead by 30-65% for multimodal LLM inference through sparsity-aware edge-cloud offloading while preserving accuracy.
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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.
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MSAO: Adaptive Modality Sparsity-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
MSAO cuts end-to-end latency by 30% and resource overhead by 30-65% for multimodal LLM inference through sparsity-aware edge-cloud offloading while preserving accuracy.
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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.