LightGP

Lightweight Gaussian Process inference in C++ with Python bindings. Metal, CUDA, and CPU backends. Composable kernels. Zero dependencies beyond numpy.

GP posterior on synthetic sin(x) data: blue mean and 95% credible band from lightgp.GPExact with an lightgp.RBF kernel. Uncertainty widens through the training-data gap near x = 1 and beyond the training range — exactly what a calibrated GP should do.

import numpy as np
import lightgp as gp

X = np.linspace(-3, 3, 100, dtype=np.float32).reshape(-1, 1)
y = np.sin(X[:, 0]).astype(np.float32) + 0.1 * np.random.randn(100).astype(np.float32)

model = gp.GPExact(gp.Scale(gp.RBF()) + gp.Scale(gp.Periodic()))
model.fit(X, y)
model.optimize(steps=200)
pred = model.predict(X)   # {'mean': array, 'var': array}

Getting Started

Install LightGP and run your first GP regression in five minutes.

Installation

Tutorials

Step-by-step guides covering regression, kernel composition, sparse GPs, and large-scale (SKI) inference.

Tutorials

API Reference

Reference for all kernels, mean functions, models, solvers, and backends.

API Reference

Why LightGP?

• No PyTorch, no TensorFlow, no Python runtime requirement. The core is dependency-free C++17. The Python layer is a thin pybind11 binding.

• Apple Silicon-native. Metal compute shaders and Apple Accelerate (AMX) for Cholesky, GEMM, and Toeplitz FFT — all auto-detected on macOS.

• CUDA-native. cuBLAS, cuSOLVER, cuFFT — all auto-detected on Linux+NVIDIA.

• Three inference paths: exact Cholesky, matrix-free CG, sparse VFE, and SKI (KISS-GP) for N > 100,000.

• Composable kernels: RBF, Matérn (½, 3/2, 5/2), Periodic, Linear, plus +, *, and Scale for arbitrary composition.

• Honest benchmarking against GPyTorch on the same hardware — see the benchmarks.

Citation

If you use LightGP in academic work, please cite the accompanying arXiv preprint:

@misc{fang2026lightgp,
  title         = {LightGP: Lightweight Gaussian Process Inference in C++ on Metal and CUDA},
  author        = {Yu-Hsueh Fang},
  year          = {2026},
  eprint        = {2605.17898},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  doi           = {10.48550/arXiv.2605.17898},
  url           = {https://arxiv.org/abs/2605.17898}
}