FlashInfer is a kernel library for GPU-accelerated inference that provides high-performance implementations of attention, GEMM, and mixture-of-experts operations for large language model serving.
The library addresses the challenge of achieving efficient inference across diverse GPU architectures by providing unified APIs that automatically select optimal backend implementations including FlashAttention-2/3, cuDNN, CUTLASS, and TensorRT-LLM. It supports multiple inference scenarios including prefill, decode, and mixed batching, with specialized kernels for paged and ragged KV-cache management, low-precision compute with FP8 and FP4 quantization, and advanced attention patterns like multi-latent attention and cascade attention for memory-efficient hierarchical caching.
Developers should adopt FlashInfer if they are building production LLM serving systems that require state-of-the-art performance across GPU generations from Turing onwards. The tool suits projects needing flexible backend selection, low-latency serving with CUDAGraph and torch.compile compatibility, or specialized operations like mixture-of-experts routing with multiple strategies. It is particularly valuable for systems handling dynamic batch serving with memory constraints or requiring quantized inference.
Almost all open issues are raised by outside users rather than the core team, indicating a substantial base of adopters reporting real-world use. Maintainers typically respond to new issues and pull requests within a day. Work in the issue tracker is dominated by attention operations, bug reports, and mixture-of-experts functionality.
