Fused Kernels in LLMs: Reducing Memory Bandwidth Bottlenecks Through GPU Kernel Fusion

Short summary

Kernel fusion combines multiple GPU operations into a single kernel, dramatically reducing memory traffic—often the primary bottleneck in LLM inference. By keeping intermediate computations in fast registers instead of slow global memory, fusion boosts arithmetic intensity and throughput without changing the model. Examples like FlashAttention show this optimization is critical for scaling modern AI systems.

• •Kernel fusion reduces memory traffic by combining multiple GPU operations into single kernels
• •Keeps intermediate values in fast registers instead of global memory, improving performance
• •Critical optimization for modern LLM inference; tools like Triton automate the process

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