CMU Researchers Introduce Sequoia: A Scalable, Robust, and Hardware-Aware Algorithm for Speculative Decoding

Efficiently supporting LLMs is becoming more critical as large language models (LLMs) become widely used. Since getting a new token involves getting all of the LLM’s parameters, speeding up LLM inference is difficult. The hardware is underutilized throughout generation due to this I/O constraint. Offloading-based inference and small-batch inference settings worsen this problem because, on […]

The post CMU Researchers Introduce Sequoia: A Scalable, Robust, and Hardware-Aware Algorithm for Speculative Decoding appeared first on MarkTechPost.

ai shorts algorithm applications artificial intelligence become cmu decoding editors pick hardware inference language language model language models large language large language model large language models llm llms parameters researchers robust scalable sequoia staff tech news technology token