The Synergy of Speculative Decoding and Batching in Serving Large . . . Batching and speculative decoding are two techniques to improve GPU hardware utilization in LLM inference To study their synergy, we implement a prototype implementation and perform an extensive characterization analysis on various LLM models and GPU architectures
The Synergy of Speculative Decoding and Batching in Serving Large . . . Batching and speculative decoding are two techniques to improve the GPU hardware uti-lization in LLM inference To study their synergy, we implement a prototype implementation and perform an extensive characterization analysis on various LLM models and GPU architectures
The Synergy of Speculative Decoding and Batching in Serving Large . . . Batching and speculative decoding are two techniques to improve GPU hardware utilization in LLM inference To study their synergy, we implement a prototype implementation and perform an extensive characterization analysis on various LLM models and GPU architectures
The Synergy of Speculative Decoding and Batching in Serving Large . . . Batching and speculative decoding are two techniques to improve GPU hardware utilization in LLM inference To study their synergy, we implement a prototype implementation and perform an extensive characterization analysis on various LLM models and GPU architectures
The Synergy of Speculative Decoding and Batching in . . . Article "The Synergy of Speculative Decoding and Batching in Serving Large Language Models" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST")
Batch Speculative Decoding Done Right - OpenReview We show that several existing batch implementations violate output equivalence—the fundamental requirement that speculative decoding must produce identical token sequences to standard autoregressive generation These violations occur precisely due to improper handling of the ragged tensor problem
The Synergy of Speculative Decoding and Batching in Serving Large . . . Batching and speculative decoding are two techniques to improve GPU hardware utilization in LLM inference To study their synergy, we implement a prototype implementation and perform an extensive characterization analysis on various LLM models and GPU architectures
The Synergy of Speculative Decoding and Batching in Serving Large . . . A prototype of online speculative decoding based on knowledge distillation is developed and evaluated using both synthetic and real query data, showing a substantial increase in the token acceptance rate and a substantial reduction in latency