[R] Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding - Carnegie Mellon University 2024 - Allows running an unquantized Llama2-70B on an RTX4090 with half-second per token latency!

Paper: [https://arxiv.org/abs/2402.12374](https://arxiv.org/abs/2402.12374)

Github: [https://github.com/Infini-AI-Lab/Sequoia/tree/main](https://github.com/Infini-AI-Lab/Sequoia/tree/main)

Abstract:

>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