ConfigSpec shows that optimal configurations for speculative LLM inference conflict across goodput (favoring smallest drafters at device-specific K=2-10), cost (favoring largest drafters at K=2), and energy (favoring smallest drafters at K=2), requiring profiling-based selection instead of fixed or
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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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ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving
ConfigSpec shows that optimal configurations for speculative LLM inference conflict across goodput (favoring smallest drafters at device-specific K=2-10), cost (favoring largest drafters at K=2), and energy (favoring smallest drafters at K=2), requiring profiling-based selection instead of fixed or
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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.