tinygrad is a deep learning framework that provides a full-stack solution from tensor operations and automatic differentiation to a custom compiler stack that generates optimized GPU kernels for multiple backends (CUDA, HIP, Metal, WebGPU, etc.). It includes a JIT compiler (TinyJit), a universal operation IR (UOp) for graph rewriting and custom kernel authoring, and a suite of neural network layers, optimizers, and datasets. The repository also contains extensive MLPerf benchmark implementations, a LLM server with streaming, and low-level hardware interaction tools (AMD firmware flashing, GPU profiling). It is primarily a software library for training and inference, with a strong research component in kernel optimization and hardware exploration.