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:
pip install flashinfer-bench
Import FlashInfer-Bench:
import flashinfer_bench as fib
print(fib.__version__)
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
Collaborators
Our collaborators include:
1 maintainer
Metadata
1 maintainer
Release files for flashinfer-bench 0.1.2
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Source distribution (sdist)
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Built distribution (wheel)
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|---|---|---|---|---|
| flashinfer_bench-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / flashinfer_bench-0.1.2.tar.gz
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| Tags | Python 3 |
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Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
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