JSTprove is a production-grade zero-knowledge proof system for verifying ONNX neural network inference. It compiles ML models into arithmetic circuits using the Expander Compiler Collection (ECC) frontend, generates witnesses, and produces proofs via a GKR/sumcheck-based prover with multiple polynomial commitment schemes (KZG, Hyrax, Orion, Basefold, WHIR). The system supports multiple efficient field arithmetics (Mersenne31, BabyBear, Goldilocks, BN254, GF2) with SIMD/AVX/NEON optimizations, and provides hardware acceleration through Metal and CUDA backends. The codebase is organized as a Rust workspace with ~30 crates, offering a CLI (`jstprove`, `jstprove-remainder`), Python bindings via PyO3, and a zkVM guest (Jolt). It is actively maintained by Inference Labs with regular commits, CI/CD pipelines, and e2e tests covering full model pipelines (LeNet, CIFAR CNN, MiniResNet). While primarily an application toolkit, it contains deep cryptographic research contributions in the form of advanced polynomial commitment constructions (sparse WHIR, hyper-bivariate KZG) and circuit optimizations (cross-layer GKR, LogUp range checks). Some hints (e.g., sigmoid, gridsample) carry documented soundness caveats, and the system relies on nightly Rust and platform-specific intrinsics, which may limit portability.