Tynx is a small, self-contained ONNX runtime with a PyTorch-shaped API for inference and training.The .whl is less than 20MB. GPU execution uses Burn/CubeCL + wgpu enabling it to run across OSes and GPUs, without requiring any extra libraries. $ pip install tynx And API import tynx as tx model = tx.nn.Sequential( tx.nn.Linear(8, 16), tx.nn.ReLU(), tx.nn.Linear(16, 2) ) optimizer = tx.optim.Adam(model.parameters(), lr=1e-3) loss = tx.nn.functional.cross_entropy(model(x), target) loss.backward() optimizer.step() The runtime is written in Rust, and also can compile to the browser(PoC done).It’s early, I d love for feedback and possible use cases API expansion where this could be beneficial.