flashinfer-ai/flashinfer
FlashInfer: Kernel Library for LLM Serving
Star History
Add Live Star History & Verified Badges to README.md
Keep your repository README looking professional and dynamic. As our continuous crawler records new stars, these official SVG badges update in real time with zero maintenance.
Renders your high-resolution star trajectory chart right inside your GitHub README or project docs.
[](https://githubrepo.cloud/repo/flashinfer-ai/flashinfer?utm_source=readme_chart)Compact Shields-style badges for your README header. Shows real-time stars and global ranking.
[](https://githubrepo.cloud/repo/flashinfer-ai/flashinfer?utm_source=readme_badge) [](https://githubrepo.cloud/repo/flashinfer-ai/flashinfer?utm_source=readme_badge) [](https://githubrepo.cloud/repo/flashinfer-ai/flashinfer?utm_source=readme_badge)Momentum
+167
STARS · LAST 30 DAYS
6
PER DAY
#2
MOST-STARRED Cuda
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +42 | +167 | +540 |
| Per day | 6 | 6 | 6 |
| Forks gained | +8 | +32 | +80 |
flashinfer gained 167 stars in the last 30 days, about 6 a day, and now has 6.7K. It is about 3 years old and has averaged roughly 2.2K stars a year. It ranks #2 among Cuda repositories and #1,845 across all languages on GitHubRepo.
Trending Record
1
DAYS ON TRENDING
#5279
BEST RANK
Oct 18, 2026
FIRST APPEARANCE
Active
STATUS TODAY
flashinfer has maintained a continuous presence across global trending indexes, peaking at #5279. Below is the 30-day activity profile:
💡 Overview
flashinfer is an open-source project written in Cuda: FlashInfer: Kernel Library for LLM Serving.
Engineered for speed, consistency, and developer ease, it solves common hurdles in attention, cuda, distributed-inference. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Cuda for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ git clone https://github.com/flashinfer-ai/flashinfer.git
cd flashinfer⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Cuda environment and standard tooling
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
flashinfer coordinates its core functionality through a modular Cuda pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with flashinfer.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install flashinfer using your package manager: `git clone https://github.com/flashinfer-ai/flashinfer.git`
Initialize your project workspace or configuration file for flashinfer.
Import flashinfer into your codebase or invoke it directly from your terminal.
Execute your test suite or run `flashinfer --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Cuda, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
flashinfer-ai/flashinfer is currently ranked #1,845 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #1,840 | apache/storm | Java | ★ 6.7K | Compare ↗ |
| #1,841 | steipete/summarize | TypeScript | ★ 6.7K | Compare ↗ |
| #1,842 | pig-mesh/pig | Java | ★ 6.7K | Compare ↗ |
| #1,843 | google-ai-edge/LiteRT-LM | C++ | ★ 6.7K | Compare ↗ |
| #1,844 | pardeike/Harmony | C# | ★ 6.7K | Compare ↗ |
| #1,845 | flashinfer-ai/flashinfer This Project | Cuda | ★ 6.7K | |
| #1,846 | magnitudedev/magnitude | Rust | ★ 6.7K | Compare ↗ |
| #1,847 | python-pendulum/pendulum | Python | ★ 6.7K | Compare ↗ |
| #1,848 | ibis-project/ibis | Python | ★ 6.7K | Compare ↗ |
| #1,849 | mizorewww/laya-mlx | Python | ★ 6.7K | Compare ↗ |
| #1,850 | x402-foundation/x402 | TypeScript | ★ 6.7K | Compare ↗ |
Frequently Asked Questions
What does flashinfer do? +
FlashInfer: Kernel Library for LLM Serving
What language is flashinfer written in? +
The primary language is Cuda. Topics include: attention, cuda, distributed-inference, gpu, jit.
Is flashinfer actively maintained? +
Yes, the last recorded push was on Oct 18, 2026 with 1,295 open issues being tracked.
How many stars does flashinfer have? +
flashinfer has 6,679 stars and 1,602 forks on GitHub.
How does flashinfer rank among GitHub repositories? +
With 6,679 stars, flashinfer-ai/flashinfer is ranked #1,845 globally across all repositories tracked on GitHubRepo and #2 among Cuda projects.
What license is flashinfer distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
