flashinfer-ai/flashinfer

High-Performance GPU Kernels for Inference

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FlashInfer is a library and kernel generator for inference that delivers state-of-the-art performance across diverse GPU architectures. It provides unified APIs for attention, GEMM, and MoE operations with multiple backend implementations including FlashAttention-2/3, cuDNN, CUTLASS, and TensorRT-LLM.

Why FlashInfer?

• State-of-the-art Performance: Optimized kernels for prefill, decode, and mixed batching scenarios
• Multiple Backends: Automatically selects the best backend for your hardware and workload
• Modern Architecture Support: Support for SM75 (Turing) and later (through Blackwell)
• Low-Precision Compute: FP8 and FP4 quantization for attention, GEMM, and MoE operations
• Production-Ready: CUDAGraph and torch.compile compatible for low-latency serving

Core Features

Attention Kernels

• Paged and Ragged KV-Cache: Efficient memory management for dynamic batch serving
• Decode, Prefill, and Append: Optimized kernels for all attention phases
• MLA Attention: Native support for DeepSeek's Multi-Latent Attention
• Cascade Attention: Memory-efficient hierarchical KV-Cache for shared prefixes
• Sparse Attention: Block-sparse and variable block-sparse patterns
• POD-Attention: Fused prefill+decode for mixed batching

GEMM & Linear Operations

• BF16 GEMM: BF16 matrix multiplication for SM10.0+ GPUs.
• FP8 GEMM: Per-tensor and groupwise scaling
• FP4 GEMM: NVFP4 and MXFP4 matrix multiplication for Blackwell GPUs
• Grouped GEMM: Efficient batched matrix operations for LoRA and multi-expert routing

Mixture of Experts (MoE)

• Fused MoE Kernels
• Multiple Routing Methods: DeepSeek-V3, Llama-4, and standard top-k routing
• Quantized MoE: FP8 and FP4 expert weights with block-wise scaling

Sampling & Decoding

• Sorting-Free Sampling: Efficient Top-K, Top-P, and Min-P without sorting
• Speculative Decoding: Chain speculative sampling support

Communication

• AllReduce: Custom implementations
• Multi-Node NVLink: MNNVL support for multi-node inference
• NVSHMEM Integration: For distributed memory operations

Other Operators

• RoPE: LLaMA-style rotary position embeddings (including LLaMA 3.1)
• Normalization: RMSNorm, LayerNorm, Gemma-style fused operations
• Activations: SiLU, GELU with fused gating

GPU Support

| Architecture | Compute Capability | Example GPUs | |--------------|-------------------|------| | Turing | SM 7.5 | T4, RTX 20 series | | Ampere | SM 8.0, 8.6 | A100, A10, RTX 30 series | | Ada Lovelace | SM 8.9 | L4, L40, RTX 40 series | | Hopper | SM 9.0 | H100, H200 | | Blackwell | SM 10.0, 10.3 | B200, B300 | | Blackwell | SM 11.0 | Jetson Thor | | Blackwell | SM 12.0, 12.1 | RTX 50 series, DGX Spark |

Note: Not all features are supported across all compute capabilities.

News

Latest:

Notable updates:

• [2025-10-08] Blackwell support added in v0.4.0
• [2025-03-10] Blog Post Sorting-Free GPU Kernels for LLM Sampling, which explains the design of sampling kernels in FlashInfer.

Getting Started

Installation

Quickstart:

pip install flashinfer-python

Package Options:

• flashinfer-python: Core package that compiles/downloads kernels on first use
• flashinfer-cubin: Pre-compiled kernel binaries for all supported GPU architectures
• flashinfer-jit-cache: CUDA-specific shim that installs architecture-specific pre-built kernel providers

For faster initialization and offline usage, install the optional packages to have most kernels pre-compiled:

pip install flashinfer-python
flashinfer install-cubin-wheel
flashinfer install-jit-cache-wheel

For Blackwell (SM100+) CuTe DSL kernels, install with the CUDA 13 extra to enable Blackwell-optimized kernels:

pip install flashinfer-python[cu13]

Verify Installation

flashinfer show-config

Basic Usage

import torch
import flashinfer
Single decode attention
q = torch.randn(32, 128, device="cuda", dtype=torch.float16)  # [num_qo_heads, head_dim]
k = torch.randn(2048, 32, 128, device="cuda", dtype=torch.float16)  # [kv_len, num_kv_heads, head_dim]
v = torch.randn(2048, 32, 128, device="cuda", dtype=torch.float16)
output = flashinfer.single_decode_with_kv_cache(q, k, v)

See documentation for comprehensive API reference and tutorials.

Install from Source

git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
cd flashinfer
python -m pip install -v .

For development, install in editable mode:

python -m pip install --no-build-isolation -e . -v

Note: When using --no-build-isolation, pip does not automatically install build dependencies. FlashInfer requires setuptools>=77. If you encounter an error like AttributeError: module 'setuptools.build_meta' has no attribute 'prepare_metadata_for_build_editable', upgrade pip and setuptools first:

> python -m pip install --upgrade pip setuptools

Build optional packages:

# flashinfer-cubin
python -m build --no-isolation --wheel flashinfer-cubin
python -m pip install flashinfer-cubin/dist/*.whl

Build one JIT-cache provider for the target GPU, then build a shim that depends on that provider. The example below builds an SM90a provider; both wheels must use the same version settings.

export FLASHINFER_JIT_CACHE_PROVIDER_ARCH=9.0a
python -m build --no-isolation --wheel flashinfer-jit-cache-provider
export FLASHINFER_JIT_CACHE_PROVIDER_ARCHS="9.0a"
python -m build --no-isolation --wheel flashinfer-jit-cache
python -m pip install \
  flashinfer-jit-cache-provider/dist/*.whl \
  flashinfer-jit-cache/dist/*.whl

For more details, see the Install from Source documentation.

Nightly Builds

pip install -U --pre flashinfer-python --index-url https://flashinfer.ai/whl/nightly/ --no-deps
pip install flashinfer-python  # Install dependencies from PyPI
flashinfer install-cubin-wheel --nightly
flashinfer install-jit-cache-wheel --nightly

CLI Tools

FlashInfer provides several CLI commands for configuration, module management, and development:

# Verify installation and view configuration
flashinfer show-config
List and inspect modules
flashinfer list-modules
flashinfer module-status
Manage artifacts and cache
flashinfer download-cubin
flashinfer install-cubin-wheel
flashinfer install-jit-cache-wheel
flashinfer download-kernels
flashinfer clear-cache
For developers: generate compile_commands.json for IDE integration
flashinfer export-compile-commands [output_path]

For complete documentation, see the CLI reference.

API Logging

FlashInfer provides comprehensive API logging for debugging. Enable it using environment variables:

# Enable logging (levels: 0=off (default), 1=basic, 3=detailed, 5=statistics)
export FLASHINFER_LOGLEVEL=3
Set log destination (stdout (default), stderr, or file path)
export FLASHINFER_LOGDEST=stdout

For detailed information about logging levels, configuration, and advanced features, see Logging in our documentation.

Custom Attention Variants

Users can customize their own attention variants with additional parameters. For more details, refer to our JIT examples.

CUDA Support

Supported CUDA Versions: 12.9, 13.0, and 13.4 (PyTorch nightly)

Note: FlashInfer strives to follow PyTorch's supported CUDA versions plus the latest CUDA release.

CUDA 13.4 wheels are built with the preview toolkit and PyTorch nightly; runtime CI currently covers CUDA 12.9 and 13.0.

Adoption

FlashInfer powers inference in:

• SGLang
• vLLM
• TensorRT-LLM
• TGI (Text Generation Inference)
• MLC-LLM
• LightLLM
• lorax
• ScaleLLM

Acknowledgement

FlashInfer is inspired by FlashAttention, vLLM, stream-K, CUTLASS, and AITemplate.

Citation

If you find FlashInfer helpful in your project or research, please consider citing our paper:

@article{ye2025flashinfer,
    title = {FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving},
    author = {
      Ye, Zihao and
      Chen, Lequn and
      Lai, Ruihang and
      Lin, Wuwei and
      Zhang, Yineng and
      Wang, Stephanie and
      Chen, Tianqi and
      Kasikci, Baris and
      Grover, Vinod and
      Krishnamurthy, Arvind and
      Ceze, Luis
    },
    journal = {arXiv preprint arXiv:2501.01005},
    year = {2025},
    url = {https://arxiv.org/abs/2501.01005}
}