Dissecting FlashInfer - A Systems Perspective on High-Performance LLM Inference | yadnyesh’s blog
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Highlights
inference efficiency is dictated by how we map computation to hardware. ⤴️
challenge is executing them with minimal memory movement, maximal kernel fusion and predictable latency across heterogeneous batches. ⤴️
FlashInfer is a response to kernel-level fragmentation in that gap. Rather than forcing every inference framework to reimplement attention from scratch, it provides a unified kernel interface optimized for modern GPU execution patterns. ⤴️
FlashInfer positions itself as an intermediate layer between inference frameworks and the actual kernel implementations that execute on GPUs. ⤴️
frameworks like vLLM, SGLang, TRT-LLM, MLC-LLM, and proprietary systems integrate with FlashInfer’s API at the top, while FlashInfer routes those calls to appropriate kernel implementations at the bottom (whether TensorRT-LLM kernels, cuDNN kernels, or FlashInfer’s own native implementations). ⤴️