VaultGemma: The world's most capable differentially private LLM (opens in new tab)

VaultGemma represents a significant milestone in privacy-preserving AI as the most capable large language model trained from scratch using differential privacy (DP). By establishing new scaling laws specifically for DP training, researchers have optimized the complex trade-offs between compute, privacy budgets, and model utility. The resulting 1-billion-parameter model demonstrates that high-performance generative AI can be achieved while maintaining rigorous mathematical guarantees against data memorization.

Scaling Laws for Differentially Private Training

• Performance in DP-trained models is primarily governed by the "noise-batch ratio," which measures the amount of random privacy noise relative to the size of the training data groups.
• Research suggests that for any given compute and privacy budget, there exists an optimal training configuration that balances model size, iterations, and batch size to achieve the lowest possible training loss.
• A critical finding indicates that DP training requires a departure from standard scaling practices, favoring significantly larger batch sizes and smaller model architectures than traditional non-DP training.

Synergies in Privacy, Compute, and Data

• Increasing the privacy budget (epsilon) in isolation leads to diminishing returns unless it is paired with a proportional increase in compute (FLOPs) or data (tokens).
• Visualizations of the scaling laws show that different model sizes can provide similar utility if the number of training iterations and batch sizes are correctly adjusted.
• The optimal configuration shifts between investing in larger models versus more iterations depending on the specific constraints of the data and privacy budgets.

Training at Scale with Algorithmic Advancements

• VaultGemma is built on the Gemma 2 architecture and utilizes a 1B parameter setup optimized for the unique constraints of DP.
• To overcome hardware limitations when processing the massive batch sizes required for DP training, the team developed a "Virtual Batch" technique in JAX to aggregate gradients across multiple steps.
• Training from scratch allows the model to outperform traditional DP-finetuned models, which often struggle to balance utility with the noise introduced during the fine-tuning process.

Performance and Evaluation

• VaultGemma achieves competitive results against standard 1B parameter models while providing formal privacy protections.
• The model demonstrates superior privacy-utility trade-offs, proving that carefully scaled DP models can retain high levels of reasoning and language capability.
• The release includes the model weights and a comprehensive technical report to assist the community in developing the next generation of private-by-design AI.

VaultGemma provides a practical blueprint for developers who need to balance the power of large language models with strict data confidentiality requirements. By leveraging the provided scaling insights, organizations can now train models that are mathematically resistant to data leakage without sacrificing significant performance.