This repository, NVlabs/tiny-cuda-nn, is a high-performance C++/CUDA library and PyTorch extension for training and querying neural networks with a focus on extreme GPU efficiency. It provides a modular, JSON-configurable framework of specialized components: input encodings (multiresolution hash grids, spherical harmonics, frequency, etc.), network architectures (fully fused MLP using Tensor Cores, CUTLASS-based MLP), loss functions, and optimizers (Adam, Shampoo, Novograd, etc.). The library supports runtime JIT compilation to fuse forward, backward, and loss kernels into single CUDA kernels, yielding up to 5x speedups. It is the engine behind NVIDIA's Instant Neural Graphics Primitives (Instant NGP) and is widely used in NeRF and neural rendering research. The codebase is production‑quality, with extensive use of templates, GPU memory arenas, CUDA graphs, multi‑stream parallelism, and comprehensive CI across many CUDA versions and GPU architectures. Although primarily a library, it includes sample applications and benchmarks that demonstrate its use in image overfitting and throughput measurement. PyTorch bindings via pybind11 make it accessible for Python‑based research workflows.