JOLT Atlas

JOLT Atlas is a zero-knowledge machine learning (zkML) framework that extends the JOLT proving system to support ML inference verification from ONNX models.

Made with ❤️ by ICME Labs.

Overview

JOLT Atlas enables practical zero-knowledge machine learning by leveraging Just One Lookup Table (JOLT) technology. Traditional circuit-based approaches are prohibitively expensive when representing non-linear functions like ReLU and SoftMax. Lookups eliminate the need for circuit representation entirely.

In JOLT Atlas, we eliminate the complexity that plagues other approaches: no quotient polynomials, no byte decomposition, no grand products, no permutation checks, and most importantly — no complicated circuits.

Our core ethos is to reduce commitment costs via sumcheck while committing only to small-value polynomials.

Examples

Examples live in jolt-atlas-core/examples/ and demonstrate end-to-end prove → verify flows for various ONNX models.

nanoGPT

A ~0.25M-parameter GPT model (4 transformer layers). Loads the ONNX graph, generates a SNARK proof of inference, and verifies it.

cargo run --release --package jolt-atlas-core --example nanoGPT

Transformer (self-attention)

Single self-attention block proof.

cargo run --release --package jolt-atlas-core --example transformer

MiniGPT / MicroGPT

Smaller GPT variants useful for quick iteration and debugging.

cargo run --release --package jolt-atlas-core --example minigpt
cargo run --release --package jolt-atlas-core --example microgpt

Benchmarks

System specs: MacBook Pro M3, 16GB RAM

nanoGPT (~0.25M params, 4 transformer layers)

nanoGPT is the standard workload we use for cross-project comparison. It is a ~250k-parameter GPT model with 4 transformer layers.

JOLT Atlas end-to-end proving breakdown:

ezkl on the same model (source):

JOLT Atlas produces a proof for nanoGPT in ~14 s versus ezkl's ~237 s proof time (not counting their 400+ s of key generation). That is roughly a 17× speed-up on proof generation alone.

GPT-2 (125M params)

GPT-2 is a 125-million-parameter transformer model from OpenAI.

JOLT Atlas end-to-end proving breakdown:

How to reproduce locally

# from repo root
cargo run --release --package jolt-atlas-core --example nanoGPT

Add -- --trace for Chrome Tracing JSON output (view in chrome://tracing), or -- --trace-terminal for timing printed to the terminal.

Getting Started

1. Clone the repository
2. Install Rust and Cargo
3. Run an example:

   cargo run --release --package jolt-atlas-core --example nanoGPT
   

GPT-2

GPT-2 uses a Hugging Face–hosted ONNX model that is not checked into the repo. A helper script downloads and prepares it automatically.

1. Download the model

# Create a virtual environment (one-time)
python3 -m venv .venv
source .venv/bin/activate

# Run the download script
python scripts/download_gpt2.py

This exports GPT-2 via Hugging Face Optimum into atlas-onnx-tracer/models/gpt2/ and copies model.onnx → network.onnx.

2. Test the model (trace only, no proof)

cargo run --release --package atlas-onnx-tracer --example gpt2

You should see the model graph printed and an output shape like [1, 16, 65536] (vocab size 50257 padded to the next power of two).

3. Prove & verify GPT-2

cargo run --release --package jolt-atlas-core --example gpt2

A successful run prints Proof verified successfully!.

Acknowledgments

Thanks to the Jolt team for their foundational work. We are standing on the shoulders of giants.