FIELD NOTES TOPIC

speculative decoding

4 notes, newest first.

  1. Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts

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    Researchers propose a new method called EcoSpec for optimizing the performance of Large Language Models (LLMs) using Sparse Mixture-of-Experts (MoE). This method aims to improve decoding speed by considering the cost…

  2. A Practical Investigation of Training-free Relaxed Speculative Decoding

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    The paper investigates training-free methods to speed up the sampling process of autoregressive Language Models (LLMs) by relaxing some strict guarantees in standard speculative decoding. The authors provide insights…

  3. A Practical Investigation of Training-free Relaxed Speculative Decoding

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    The study investigates training-free methods of relaxed speculative decoding, which can speed up sampling from an autoregressive Language Model (LLM) by using a faster auxiliary model to draft tokens that are then…

  4. Boost Inference Performance up to 15x on NVIDIA Blackwell Using DFlash Speculative Decoding

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    NVIDIA announced DFlash Speculative Decoding, an inference optimization technique for Blackwell GPUs targeting multi-agent AI workloads. The claimed performance improvement is up to 15x on NVIDIA Blackwell hardware.