March 13, 2024, 4:35 p.m. | /u/Singularian2501

Machine Learning

Paper: [](

Github: [](


>As the usage of large language models (LLMs) grows, performing efficient inference with these models becomes increasingly important. While speculative decoding has recently emerged as a promising direction for speeding up inference, existing methods are limited in their ability to scale to larger speculation budgets, and adapt to different hyperparameters and hardware. This paper introduces Sequoia, a scalable, robust, and hardware-aware algorithm for speculative decoding. To attain better scalability, Sequoia introduces a dynamic programming algorithm …

abstract adapt budgets decoding hardware inference language language models large language large language models llms machinelearning paper robust scalable scale sequoia speculation usage

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