Google released VaultGemma 1B, a 1 billion parameter language model that implements differential privacy throughout the entire training process, not just fine-tuning. The model uses a 26-layer decoder-only transformer architecture trained on 13 trillion tokens with strict privacy guarantees (ε ≤ 2.0). While it shows measurable performance trade-offs compared to non-private models, scoring lower on benchmarks like ARC and TriviaQA, it demonstrates that differentially private training is now practical at billion-parameter scale without detectable memorization of training data.