DeepProve

Zero-knowledge proof system for neural network inference, with first-class support for end-to-end LLM proving.

👉 Looking to run DeepProve? Start with zkml/README.md

That's where the installation steps, model setup, GPU build, and the full end-to-end bench-llm tutorial live. The rest of this page is a high-level summary of what DeepProve is and what to expect.

Overview

DeepProve is the first end-to-end zero-knowledge proof system for full LLM inference. It generates cryptographic proofs of neural network forward passes using sumchecks and logup GKR, achieving sublinear proving time in model size — orders of magnitude faster than circuit-based approaches.

Confirmed working models: GPT-2, Gemma 3, Llama 2 — all transformer layers proven end-to-end, from token embeddings through to next-token argmax. MLP and CNN inference is also supported.

This repository is a Rust workspace. The zkml crate is the core proving library; the remaining crates provide the client stack, storage layer, and developer tooling.

Headline Numbers

Single-machine inference proving on a 24-core / 504 GB CPU server:

• 10–30× faster than the previous published state of the art (e.g. zkGPT reports ≈ 0.05 tokens/s on similar hardware).
• Accuracy preserved: ≥99.6% cosine similarity to the floating-point baseline at 12-bit quantization (GPT-2).
• Scales out: horizontal proof distribution and GPU acceleration are supported today; clusters of GPU workers are on the roadmap.

For the full methodology and a deeper benchmark sweep across sequence lengths and models, see the DeepProve paper (link to be added) and zkml/README.md.

Repository Structure

Licensing

Licensed under the Lagrange License.

Acknowledgements

This project builds upon the work from scroll-tech/ceno, reusing the sumcheck and GKR implementation from that codebase.