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arXiv · 2602.11808

Deep Kernel Fusion for Transformers

Zixi Zhang·Zhiwen Mo·Yiren Zhao·Robert Mullins
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Abstract

Agentic LLM inference with long contexts is increasingly limited by memory bandwidth rather than compute. In this setting, SwiGLU MLP blocks, whose large weights exceed cache capacity, become a major yet under-optimized bottleneck. We propose DeepFusionKernel, a deeply fused kernel that cuts HBM traffic and boosts cache reuse, delivering up to 13.2% speedup on H100 and 9.7% on A100 over SGLang. Integrated with SGLang and paired with a kernel scheduler, DeepFusionKernel ensures consistent accelerations over generation lengths, while remaining adaptable to diverse models, inference configurations, and hardware platforms.

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Zixi Zhang, Zhiwen Mo, Yiren Zhao, Robert Mullins. 2026-02-12. Deep Kernel Fusion for Transformers. https://arxiv.org/abs/2602.11808

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