NVIDIA CUTLASS is a comprehensive C++ template library and Python DSL for implementing high-performance CUDA kernels, primarily focused on matrix multiply (GEMM), convolution, and attention operations. It provides a layered architecture from low-level warp matrix multiply-accumulate (MMA) and copy instructions (CuTe atoms) up through threadblock-level collective mainloops and epilogues, kernel-level schedulers (persistent, stream-K, warp-specialized), and a high-level operator API for runtime dispatch with fusion support. The repository includes a Python-embedded DSL (CuTeDSL) that uses MLIR-based compilation to generate optimized CUDA kernels, a separate Python code generation backend (cutlass_cppgen) for JIT compilation, and an extensive profiling and testing infrastructure. It spans NVIDIA GPU architectures from Volta to Blackwell and is actively maintained with contributions to deep learning frameworks like PyTorch and JAX.