arxiv paper proposes a two-dimensional early exit optimization technique for reducing LLM inference latency and computational cost. Early exit methods allow models to generate predictions and halt processing before consuming all layers. This work extends existing single-dimension early exit strategies with an additional optimization axis.
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Two-dimensional early exit optimisation of LLM inference
Two-dimensional early-exit optimization extends beyond single-axis methods to cut LLM inference latency and compute cost by allowing models to exit across multiple optimization axes simultaneously.
Wednesday, April 22, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline
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