UniAttn introduces a novel approach to reduce memory overhead and inference latency in deployed large language models through softmax optimization.

• Unifies softmax operations across neural network blocks to eliminate redundant computations
• Reduces inference costs by up to 30% while maintaining model performance
• Achieves better balance between efficiency and accuracy compared to existing KV-sharing approaches
• Compatible with a variety of post-trained LLM architectures

This research addresses a critical engineering challenge for real-world LLM deployment, enabling more efficient inference without sacrificing model quality—essential for scaling AI applications in resource-constrained environments.

UniAttn: Reducing Inference Costs via Softmax Unification for Post-Training LLMs