Dynamic Rebatching for Efficient Early-Exit Inference with DREX

The article likely discusses a novel method, DREX, for optimizing inference in large language models (LLMs). The focus is on improving efficiency through dynamic rebatching, which is a technique to adjust batch sizes during inference to enable early exits from the computation when possible. This suggests a focus on reducing computational cost and latency in LLM deployments.