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Crawler view**Building the Virtuous Cycle for AI-driven LLM Systems** Get Started | Documentation | Blogpost | Slack (#flashinfer-bench) **FlashInfer-Bench** is a benchmark suite and production workflow designed to build a virtuous cycle of self-improving AI systems. It is part of a broader initiative to build the *virtuous cycle of AI improving AI systems* — enabling AI agents and engineers to collaboratively optimize the very kernels that power large language models. Installation Install FlashInfer-Bench with pip: Import FlashInfer-Bench: Get Started This guide shows you how to use FlashInfer-Bench python module with the FlashInfer-Trace dataset. FlashInfer Trace Dataset We provide an official dataset called **FlashInfer-Trace** with kernels and workloads in real-world AI system deployment environments. FlashInfer-Bench can use this dataset to measure and compare the performance of kernels. It follows the FlashInfer Trace Schema. The official dataset is on HuggingFace: https://huggingface.co/datasets/flashinfer-ai/flashinfer-trace Clone it with Git LFS pointer files only (large tensor files are downloaded on demand during benchmarking): Collaborators Our collaborators include: