{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Tilelang is a domain-specific language for writing high-performance GPU and CPU kernels (GEMM, attention, sparse operations) using Pythonic syntax, compiling to optimized code via TVM.","skillfed_tags":["gpu-kernel-compiler","dsl","performance-optimization"],"use_cases":["Implement custom GEMM kernels with layout optimization and L2-cache swizzling without writing raw CUDA","Build high-performance attention kernels (FlashAttention, MLA) in ~80 lines of Python code","Compile sparse tensor operations and dequantization kernels targeting multiple GPU architectures","Prototype and optimize AI workload kernels while maintaining portability across NVIDIA, AMD, and Apple hardware","Generate WebGPU or CuTe DSL code for emerging deployment targets without rewriting kernel logic"],"what_it_does":"Tilelang is a Python-embedded domain-specific language that lets you write GPU and CPU kernels at a higher level of abstraction than raw CUDA or HIP, while still accessing low-level optimizations. It compiles to efficient machine code via TVM, supporting multiple backends including NVIDIA CUDA, AMD HIP, Apple Metal, and emerging targets like NVIDIA CuTe DSL and Huawei Ascend. The language provides Pythonic syntax for declaring tensor shapes, allocating shared memory, managing data movement, and expressing computation patterns like matrix multiplication, attention mechanisms, and sparse operations.\n\nYou write a kernel as a Python function decorated with `@tilelang.jit`, declare tensor shapes and dtypes, allocate buffers, and express the computation using tile-level operations. Tilelang handles the compilation to optimized code for your target hardware. It integrates z3-solver for symbolic reasoning and automatic correctness verification, and recently migrated to apache-tvm-ffi to reduce CPU overhead. The package includes examples for GEMM, dequantization, FlashAttention, and MLA decoding, with tested support for NVIDIA (H100, A100, V100, RTX series), AMD (MI250, MI300X), and Apple Metal devices.","worth_installing":"Yes, if you need to write or optimize GPU kernels and prefer a higher-level language than raw CUDA/HIP. The active maintenance, permissive license, and broad hardware support make it a solid choice for AI workload optimization. However, note the Beta status, recent project age (11 months), and dependency on torch and TVM\u2014verify stability for your specific use case and target hardware before production deployment."},"id":"tilelang","links":{"html":"https://skillfed.io/packages/tilelang","md":"https://skillfed.io/packages/tilelang.md","pypi":"https://pypi.org/project/tilelang/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-03","license_spdx":"MIT","license_treatment":"permissive","name":"tilelang","python_support":"supports_current","summary":"A tile level programming language to generate high performance code."},"popularity":{"monthly_downloads":2781004,"position":2893,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.13"}