FlashFormer: Whole-Model Kernels for Efficient Low-Batch Inference

The paper introduces FlashFormer, a specialized kernel designed to accelerate single-batch inference for transformer-based large language models, addressing the needs of low-batch, latency-sensitive applications like edge deployment. It demonstrates significant speedups over existing inference kernels across different model sizes and quantization settings, highlighting its potential for improving efficiency in real-world scenarios.