New kernels boost LLM inference speed by fusing SwiGLU activations

Researchers have developed new techniques to accelerate the inference of large language models (LLMs) by fusing SwiGLU activation functions directly into GEMM operations at the tile level. These methods, implemented using custom CUTLASS kernels for NVIDIA H100 GPUs, significantly reduce the overhead associated with intermediate tensor materialization. Evaluations on Qwen 2.5 models show speedups of up to 2.47x compared to standard PyTorch implementations, achieving higher peak BF16 utilization and demonstrating numerical superiority over existing libraries like cuBLAS. AI

IMPACT This research could lead to more efficient deployment and faster response times for large language models in production environments.

RANK_REASON Academic paper detailing a novel technical approach to improve LLM inference efficiency. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• bfloat16
• cuBLAS
• CUTLASS
• GEMM
• LLM
• NVIDIA H100
• PyTorch
• Qwen 2.5
• SwiGLU

• infra
• paper

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