"Google’s VaultGemma is the first billion-parameter large language model trained from scratch with differential privacy, setting a new standard for secure, open-source AI models."
Introduction
Google and DeepMind have announced VaultGemma, a 1-billion parameter open-source language model. It stands out as the largest LLM trained entirely with differential privacy, setting a new benchmark for security in AI research and development.
Technical Innovation & Privacy
- Trained using Differentially Private Stochastic Gradient Descent (DP-SGD), with sequence-level guarantees.
- Formal privacy budget of (ε ≤ 2.0, δ ≤ 1.1e-10) providing strong mathematical privacy.
- Model trained on a 13 trillion-token dataset including web, code, and scientific text.
- Advanced scaling laws developed to predict privacy, compute, and performance trade-offs.
Performance & Accessibility
- Zero detectable memorization of training data maximizing user data safety.
- Performance comparable to top models from approximately five years ago; less capable than the latest non-private LLMs.
- Academic benchmark: ARC-C score of 26.45 vs. 38.31 for Gemma-3 1B.
- Open weights, research paper, and community resources available on Hugging Face and Kaggle.
Feature Comparison Table
| Model | Parameters | Privacy Guarantee |
|---|---|---|
| VaultGemma | 1B | (ε ≤ 2.0, δ ≤ 1.1e-10) |
| Gemma-3 1B | 1B | None |
Conclusion
VaultGemma is a major step forward for privacy-first AI, balancing transparency with robust safety measures. By releasing its weights and methodology to the community, Google is accelerating the evolution of private, ethical, and open machine learning.
