NVIDIA Introduces High-Performance FlashInfer for Efficient LLM Inference

Darius Baruo
Jun 13, 2025 11:13

NVIDIA’s FlashInfer enhances LLM inference speed and developer velocity with optimized compute kernels, offering a customizable library for efficient LLM serving engines.

NVIDIA has unveiled FlashInfer, a cutting-edge library aimed at enhancing the performance and developer velocity of large language model (LLM) inference. This development is set to revolutionize how inference kernels are deployed and optimized, as highlighted by NVIDIA’s recent blog post.

Key Features of FlashInfer

FlashInfer is designed to maximize the efficiency of underlying hardware through highly optimized compute kernels. This library is adaptable, allowing for the quick adoption of new kernels and acceleration of models and algorithms. It utilizes block-sparse and composable formats to improve memory access and reduce redundancy, while a load-balanced scheduling algorithm adjusts to dynamic user requests.

FlashInfer’s integration into leading LLM serving frameworks, including MLC Engine, SGLang, and vLLM, underscores its versatility and efficiency. The library is the result of collaborative efforts from the Paul G. Allen School of Computer Science & Engineering, Carnegie Mellon University, and OctoAI, now a part of NVIDIA.

Technical Innovations

The library offers a flexible architecture that splits LLM workloads into four operator families: Attention, GEMM, Communication, and Sampling. Each family is exposed through high-performance collectives that integrate seamlessly into any serving engine.

The Attention module, for instance, leverages a unified storage system and template & JIT kernels to handle varying inference request dynamics. GEMM and communication modules support advanced features like mixture-of-experts and LoRA layers, while the token sampling module employs a rejection-based, sorting-free sampler to enhance efficiency.

Future-Proofing LLM Inference

FlashInfer ensures that LLM inference remains flexible and future-proof, allowing for changes in KV-cache layouts and attention designs without the need to rewrite kernels. This capability keeps the inference path on GPU, maintaining high performance.

Getting Started with FlashInfer

FlashInfer is available on PyPI and can be easily installed using pip. It provides Torch-native APIs designed to decouple kernel compilation and selection from kernel execution, ensuring low-latency LLM inference serving.

For more technical details and to access the library, visit the NVIDIA blog.

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