Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond

arXiv — cs.LG•Wednesday, November 12, 2025 at 5:00:00 AM

Recent advancements in artificial intelligence have highlighted the limitations of transformers, particularly their quadratic computational cost in sequence length, which hampers efficiency in applications. The introduction of a new method for long convolution sequence models (LCSMs), specifically Hyena, marks a significant breakthrough. This method reduces the inference time complexity to quasilinear O(L log^2 L), allowing for substantial improvements in processing speed. Empirical results demonstrate an impressive end-to-end improvement of up to 7.8 times, with a remarkable 110 times enhancement in the position-mixing component. This innovation not only optimizes performance but also facilitates almost complete parallelization across layers, paving the way for more efficient AI models that can handle longer sequences without the prohibitive costs previously associated with transformers.

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