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Speculative Decoding Speeds LLM Inference Most When Models Are Co-Located
Speculative decoding can accelerate large language model inference by 1.5 to 3 times when draft and target models are co-located. However, distributing the draft model to edge devices while keeping the target model in the cloud offers limited latency benefits due to WAN communication delays.
Understanding where speculative decoding provides latency improvements helps optimize LLM deployment strategies. This insight suggests that hosting both models on the same server is more effective than splitting them across edge and cloud for inference speed.
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Score 75
Source Type arxiv
Reposts 0
Topic Quality 59
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