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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Building the Virtuous Cycle for AI-driven LLM Systems
| Date | Stars |
|---|---|
| 2026-07-31 | 264 |
| 2026-08-06 | 264 |
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<div align="center" id="top"> <picture> <source media="(prefers-color-scheme: light)" srcset="docs/logo/fib-white-bg.png"> <source media="(prefers-color-scheme: dark)" srcset="docs/logo/fib-black-bg.png"> <img src="docs/logo/fib-white-bg.png" alt="FlashInfer-Bench logo" width="400"> </picture> [](https://bench.flashinfer.ai/docs/) [](https://github.com/flashinfer-ai/flashinfer-bench/blob/main/LICENCE) [](https://pypi.org/project/flashinfer-bench/) **Building the Virtuous Cycle for AI-driven LLM Systems** [Get Started](#get-started) | [Documentation](https://bench.flashinfer.ai/docs/) | [Blogpost](https://flashinfer.ai/2025/10/21/flashinfer-bench.html) | [Slack (#flashinfer-bench)](https://join.slack.com/t/flashinfer/shared_invite/zt-379wct3hc-D5jR~1ZKQcU00WHsXhgvtA) </div> **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: ```bash pip install flashinfer-bench ``` Import FlashInfer-Bench: ```python import flashinfer_bench as fib print(fib.__version__) ``` ## Get Started This [guide](https://bench.flashinfer.ai/docs/start/quickstart) 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](https://bench.flashinfer.ai/docs/flashinfer-trace). 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): ```bash GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/flashinfer-ai/flashinfer-trace flashinfer-bench run --local flashinfer-trace ``` ## Collaborators Our collaborators include: <div align="center"> [<img src="https://raw.githubusercontent.com/mlc-ai/XGrammar-web-assets/refs/heads/main/repo/nvidia.svg" height=50/>](https://github.com/NVIDIA/TensorRT-LLM)   [<img src="https://raw.githubusercontent.com/mlc-ai/XGrammar-web-assets/refs/heads/main/repo/gpu_mode.png" height=50/>](https://github.com/gpu-mode)   [<img src="https://raw.githubusercontent.com/mlc-ai/XGrammar-web-assets/refs/heads/main/repo/sglang.png" height=50/>](https://github.com/sgl-project/sglang)   [<img src="https://raw.githubusercontent.com/mlc-ai/XGrammar-web-assets/refs/heads/main/repo/vllm.png" height=50/>](https://github.com/vllm-project/vllm)   [<img src="https://raw.githubusercontent.com/mlc-ai/XGrammar-web-assets/refs/heads/main/repo/bosch.svg" height=50/>](https://www.bosch.com/) </div>
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3a99e51e03fc360b, llm:Repository name 'flashinfer-bench' and description 'Building the Virtuous Cycle for AI-driven LLM Systems' indicate tooling for benchmarking/inference for LLM systems.
matched fp:3a99e51e03fc360b, llm:Repository name 'flashinfer-bench' and description 'Building the Virtuous Cycle for AI-driven LLM Systems' indicate tooling for benchmarking/inference for LLM systems.
matched fp:3a99e51e03fc360b, llm:Repository name 'flashinfer-bench' and description 'Building the Virtuous Cycle for AI-driven LLM Systems' indicate tooling for benchmarking/inference for LLM systems.