dsperse is a distributed Zero-Knowledge Machine Learning (zkML) toolkit that decomposes ONNX neural network models into circuit-compatible segments for verifiable AI inference. The core Rust library provides model slicing, circuit compilation (using JSTprove backend), witness generation, and proof verification. It exposes both a CLI interface and Python bindings via PyO3/Maturin for integration into ML pipelines. The architecture follows a clear separation: the slicer module handles ONNX graph decomposition and tiling strategies (channel split, dimension split, spatial tiling), the pipeline orchestrates execution with memory-efficient tensor caching, and the backend trait enables pluggable ZK proving systems. Content-addressed storage with SHA256 hashing ensures artifact integrity across distributed execution. Notable constraints: the license explicitly restricts operational use without written permission from the IP owner, and the codebase relies on nightly Rust toolchain with specific git dependencies (tract-onnx, jstprove_circuits) that create version coupling risks. Security advisories are partially ignored in the audit configuration.