High-Performance GPU Kernels for Inference

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[](https://ci.tlcpack.ai/job/flashinfer-ci/job/main/) [](https://github.com/flashinfer-ai/flashinfer/actions/workflows/build-doc.yml) **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 ## News Latest: [](https://github.com/flashinfer-ai/flashinfer/releases/latest) Notable updates: ## Getting Started ### Installation **Quickstart:** pip install flashinfer-python **Package Options:** **For faster initialization and offline usage**, install the optional packages to have most kernels pre-compiled: pip install flashinfer-python flashinfer-cubin # JIT cache (replace cu129 with your CUDA version) pip install flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu129 **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](https://docs.flashinfer.ai/) 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 Build optional packages: # flashinfer-cubin cd flashinfer-cubin python -m build --no-isolation --wheel python -m pip install dist/*.whl # flashinfer-jit-cache (customize for your target GPUs) export FLASHINFER_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 10.3a 11.0a 12.0f" cd flashinfer-jit-cache python -m build --no-isolation --wheel python -m pip install dist/*.whl For more details, see the [Install from Source documentation](https://docs.flashinfer.ai/installation.html#install-from-source). ### 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 pip install -U --pre flashinfer-cubin --index-url https://flashinfer.ai/whl/nightly/ # JIT cache (replace cu129 with your CUDA version) pip install -U --pre flashinfer-jit-cache --index-url https://flashinfer.ai/whl/nightly/cu129 ### 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 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: # 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). ## 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): @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} }