Introduction
FlashInfer is a kernel library and JIT toolset optimized for LLM serving scenarios. It provides high-performance implementations of attention and sampling, aiming to reduce latency and improve GPU bandwidth utilization. FlashInfer supports integration with PyTorch, TVM, and other frameworks, making it suitable for building high-throughput inference services.
Key Features
- Efficient sparse/dense attention kernels and sampling implementations.
- Production-ready kernel customization and JIT compilation pipeline, supporting precompilation and caching mechanisms.
- Compatible with mainstream inference frameworks (PyTorch/TVM/C++).
- Memory and operator optimization strategies tailored for LLM serving.
Use Cases
- Large-scale LLM inference services and low-latency online inference.
- Research and engineering deployment of custom attention or sampling strategies.
- Integration with inference stacks such as vLLM and TGI to optimize overall throughput.
Technical Highlights
- Provides CUDA and C++ level kernel optimizations, supporting various GPU architectures.
- Enables rapid experimentation and engineering packaging through a plugin-based JIT mechanism.