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FLASHINFER
GPU kernel library for LLM inference and serving
Apache-2.0
ABOUT
Transformer inference on GPUs is bottlenecked by attention computation, memory bandwidth, and suboptimal kernel implementations — especially for long contexts and production serving with batching. FlashInfer solves this by providing highly optimized, fused GPU kernels for attention, state-space models, and related primitives that minimize memory reads, maximize compute utilization, and deliver 2-5x speedups over naive implementations for LLM inference.
INSTALL
pip install flashinfer-pythonINTEGRATION GUIDE
1. Accelerate inference: replace standard attention with FlashInfer's fused kernels to achieve 2-5x speedups in LLM generation throughput
2. Long context support: efficiently compute attention for sequences up to 128K+ tokens with optimized memory access patterns and sparse attention support
3. Production serving: integrate with vLLM, SGLang, and other serving frameworks to speed up batched inference for deployed LLMs
4. Multi-head latent attention: leverage FlashInfer's MLA kernel support for DeepSeek-v2 and similar architectures with reduced KV cache requirements
5. Custom kernel development: build on FlashInfer's kernel primitives to create custom attention variants without writing low-level CUDA code
TAGS
gpu-kernelsinferencellmattentioncudaoptimizationservingdeep-learning