LayerSkip presents a novel optimization technique that allows for earlier exit during the inference process in Large Language Models (LLM), which results in substantial computational efficiency without compromising accuracy. Highlights include:

• Layer dropout during training to facilitate early exit from any layer during inference.
• Self-speculative decoding allows for corrections after premature exit, enhancing both performance and resource utilization.
• Demonstrated speedups in summarization tasks and other specific applications, up to 2.16 times faster than traditional methods.

Potential of Early Exit Inference:

• Reductions in the time and computational power required for model deployment.
• Feasibility for use in real-time applications where prompt responses are critical.

This innovation not only saves resources but also opens new avenues for deploying complex models in time-sensitive environments.