AI Research
Distributed LLM Inference via Communication-Efficient Speculative Decoding
Vocabulary-scale communication overhead can limit decoding speedups
Distributed speculative decoding has emerged as a promising approach for device--server collaborative large language model (LLM) inference, but its decoding speedup is often limited by the vocabulary-scale communication overhead.
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ArXiv CS.LG
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ArXiv CS.LG
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Device–server collaboration could make large language model inference more practical across distributed hardware. Reducing vocabulary-scale communication overhead is important because it currently constrains the speed gains of speculative decoding.
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