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: [](https://github.com/flashinfer-ai/flashinfer/releases/latest) Notable updates: - [2025-10-08] Blackwell support added in [v0.4.0](https://github.com/flashinfer-ai/flashinfer/releases/tag/v0.4.0) - [2025-03-10] [Blog Post](https://flashinfer.ai/2025/03/10/sampling.html) Sorting-Free GPU Kernels for LLM Sampling, which explains the design of sampling kernels in FlashInfer. ## Getting Started ### Installation **Quickstart:** ```bash 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: ```bash 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: ```bash pip install flashinfer-python[cu13] ``` ### Verify Installation ```bash flashinfer show-config ``` ### Basic Usage ```python 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](https://docs.flashinfer.ai/) for comprehensive API reference and tutorials. ### Install from Source ```bash git clone https://github.com/flashinfer-ai/flashinfer.git --recursive cd flashinfer python -m pip install -v . ``` **For development**, install in editable mode: ```bash 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: > ```bash > python -m pip install --upgrade pip setuptools > ``` Build optional packages: ```bash # 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. ```bash 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](https://docs.flashinfer.ai/installation.html#install-from-source). ### Nightly Builds ```bash 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: ```bash # 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](https://docs.flashinfer.ai/cli.html). ## API Logging FlashInfer provides comprehensive API logging for debugging. Enable it using environment variables: ```bash # 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](https://docs.flashinfer.ai/logging.html) in our documentation. ## Custom Attention Variants Users can customize their own attention variants with additional parameters. For more details, refer to our [JIT examples](https://github.com/flashinfer-ai/flashinfer/blob/main/tests/utils/test_jit_example.py). ## 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](https://github.com/sgl-project/sglang) - [vLLM](https://github.com/vllm-project/vllm) - [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) - [TGI (Text Generation Inference)](https://github.com/huggingface/text-generation-inference) - [MLC-LLM](https://github.com/mlc-ai/mlc-llm) - [LightLLM](https://github.com/ModelTC/lightllm) - [lorax](https://github.com/predibase/lorax) - [ScaleLLM](https://github.com/vectorch-ai/ScaleLLM) ## Acknowledgement FlashInfer is inspired by [FlashAttention](https://github.com/dao-AILab/flash-attention/), [vLLM](https://github.com/vllm-project/vllm), [stream-K](https://arxiv.org/abs/2301.03598), [CUTLASS](https://github.com/nvidia/cutlass), and [AITemplate](https://github.com/facebookincubator/AITemplate). ## Citation If you find FlashInfer helpful in your project or research, please consider citing our [paper](https://arxiv.org/abs/2501.01005): ```bibtex @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} } ```