FFN Fusion introduces a novel optimization technique that identifies and parallelizes sequences of Feed-Forward Network layers in LLMs, significantly enhancing computational efficiency.

• Reduces sequential computation bottlenecks by transforming serial operations into parallel ones
• Maintains model accuracy while decreasing inference latency
• Applies a principled methodology to identify fusion opportunities in existing model architectures
• Demonstrates how engineering innovations can improve LLM performance without architectural redesign

This research matters for engineering teams by offering a practical approach to optimize inference speed in production LLM deployments - critical for responsive AI systems and cost-effective scaling.

FFN Fusion: Rethinking Sequential Computation in Large Language Models