Data-protective Proofs for Next-Generation AI: Proving Gemma3 with zkPyTorch
In the AI era, large models are everywhere — from powering research to enabling next-gen applications. But every time we deploy them, we face a dilemma: how can we trust the outputs without exposing the model’s weights, data, or proprietary details?
That’s where zkPyTorch comes in. By compiling Gemma-3 into zero-knowledge friendly circuits, zkPyTorch makes it possible to cryptographically prove the correctness of Gemma-3’s outputs — without revealing the inputs or the model itself.
Why Prove Gemma-3?
Traditional AI deployments suffer from three fundamental limitations:
- Lack of Transparency: Users have no way to verify whether a model is authentic or whether its outputs are produced correctly.
- Intellectual Property Risks: Exposing model weights for validation risks leaking valuable research and proprietary knowledge.
Gemma-3 is a powerful, state-of-the-art model. But without verifiability, its adoption in sensitive and decentralized environments is limited.
What Does zkPyTorch Bring to Gemma-3?
zkPyTorch is a compiler framework that connects PyTorch to efficient zero-knowledge proof backends. When applied to Gemma-3, it delivers:
- Data-protective Proofs: Model weights remain fully local — never exposed or transmitted.
- Tamper-Proof Outputs: Cryptographic guarantees allow anyone to verify that results were generated by Gemma-3.
- Seamless Integration: Developers continue working in PyTorch as usual, while zkPyTorch transparently adds the zero-knowledge proof
Together, these capabilities make Gemma-3 suitable for deployment in contexts where trust, data protection, and security are non-negotiable.
How Does It Work?
To enable verifiable inference, Gemma-3 must be transformed from a neural network into a zk-friendly circuit. Unlike standard ML execution, where results are taken on trust, zero-knowledge proofs require computations to be represented as structured constraints. This transformation ensures that every matrix multiplication, activation, and normalization step in Gemma-3 can be cryptographically verified.
zkPyTorch transforms Gemma-3 into zero-knowledge proof circuits through three tightly integrated steps:
- ZKP-Friendly Quantization
Gemma-3 is quantized with a strategy carefully designed to preserve model accuracy. The quantized version achieves very high cosine similarity with the original Gemma-3 outputs, ensuring that its predictions remain faithful to the unquantized model while making the computations provable in zero-knowledge.
- Preprocessing
The quantized model is then transformed into a structured computational graph, enriched with auxiliary information of intermediate commitments to support proof construction. This step ensures that each layer of Gemma-3 from attention blocks to activation functions is represented in a form that can be verified cryptographically.
- Hierarchical Optimization
A multi-level optimizer then refines the model, operation, and circuit layers, improving efficiency and reducing overhead during proof generation. This is particularly impactful in the decode phase: instead of generating a proof for each token step by step, zkPyTorch introduces a batch proofing mechanism. With batch proofs, multiple tokens can be verified together within a single proof, dramatically cutting down redundancy and reducing the overall cost of proving large-scale language model inference like Gemma-3.
Through this pipeline, zkPyTorch enables Gemma-3 to be executed as a circuit, producing succinct, cryptographic proofs of inference that balance accuracy, data-protection, and verifiability.
The Future of Verifiable AI
The entire pipeline is built on top of our latest research project, zkPyTorch (ePrint 2025/535), a powerful compiler framework that bridges modern machine learning and zero-knowledge cryptography. zkPyTorch shows that large models like Gemma-3 can be efficiently proven. This unlocks a future where AI is both powerful and trustworthy, enabling data-protective applications across Web3, healthcare, finance, and beyond.