Skip Softmax is a technique designed to accelerate long-context inference in large language models (LLMs) by optimizing the attention computation process. It achieves this by dynamically pruning attention blocks that contribute minimally to the output, thereby reducing computation time without the need for retraining. This method is compatible with existing models and leverages NVIDIA's Hopper and Blackwell GPUs for enhanced performance, offering up to 1.4x speed improvements in both time-to-first-token and time-per-output-token. Skip Softmax maintains accuracy while providing substantial efficiency gains, making it a valuable tool for machine learning engineers working with long-context scenarios. This matters because it addresses the critical bottleneck of attention computation, enabling faster and more efficient deployment of LLMs at scale.
