FlashInfer is an open-source library providing highly optimized GPU kernels specifically designed for LLM inference workloads. Developed with a focus on flexibility and performance, it addresses the key computational bottlenecks in serving large language models, particularly the attention mechanism.

Core Capabilities

• FlashAttention for Inference: Implements memory-efficient attention kernels optimized specifically for the decode phase of LLM inference, where the query length is 1 but the KV cache can be very long.
• Paged KV Cache Support: Native support for paged attention — managing the key-value cache in non-contiguous memory blocks, similar to how operating systems manage virtual memory.
• Ragged Tensors: Efficiently handles variable-length sequences within a batch without padding, maximizing GPU utilization when requests have different context lengths.
• Custom Attention Variants: Supports grouped-query attention (GQA), multi-query attention (MQA), sliding window attention, and other modern attention patterns used by different model architectures.

Performance Advantages

• Kernel Specialization: Unlike general-purpose attention libraries, FlashInfer's kernels are specifically tuned for the asymmetric compute patterns of inference (short query, long KV cache).
• Composable API: Provides building-block kernels that serving frameworks can combine and customize for their specific needs.

Integration

FlashInfer is used as a backend kernel library by several popular LLM serving frameworks, including SGLang and vLLM, where it provides the low-level attention computation. Rather than being an end-to-end serving solution, FlashInfer focuses on being the fastest possible attention kernel that other systems can build upon.

It supports NVIDIA GPUs from Ampere (A100) onwards and is actively developed to support the latest hardware features and model architectures.