Google AI Releases VaultGemma: The Largest and Most Capable Open Model (1B-parameters) Trained from Scratch with Differential Privacy
Google AI Research and DeepMind have introduced VaultGemma 1B, the largest open-weight large language model trained entirely with differential privacy (DP). This advancement marks a significant step towards creating AI models that are both powerful and privacy-preserving.
Why Do We Need Differential Privacy in LLMs?
Large language models trained on extensive web-scale datasets are susceptible to memorization attacks, where sensitive or personally identifiable information can be extracted from the model. Studies indicate that verbatim training data can resurface, particularly in open-weight releases. Differential Privacy provides a mathematical guarantee that prevents any single training example from significantly influencing the model. Unlike methods that apply DP only during fine-tuning, VaultGemma enforces full private pretraining, ensuring privacy protection starts at the foundational level.
What Is the Architecture of VaultGemma?
VaultGemma’s architecture is similar to earlier Gemma models but optimized for private training:
- Model size: 1B parameters / 26 layers
- Transformer type: Decoder-only
- Activations: GeGLU with feedforward dimension of 13,824
- Attention: Multi-Query Attention (MQA) with a global span of 1024 tokens
- Normalization: RMSNorm in pre-norm configuration
- Tokenizer: SentencePiece with a 256K vocabulary
A notable change is the reduction of sequence length to 1024 tokens, which lowers compute costs and enables larger batch sizes under DP constraints.
What Data Was Used for Training?
VaultGemma was trained on the same 13 trillion-token dataset as Gemma 2, primarily composed of English text from web documents, code, and scientific articles. The dataset underwent several filtering stages to:
- Remove unsafe or sensitive content
- Reduce personal information exposure
- Prevent evaluation data contamination
This ensures safety and fairness in benchmarking.
How Was Differential Privacy Applied?
VaultGemma utilized DP-SGD (Differentially Private Stochastic Gradient Descent) with gradient clipping and Gaussian noise addition. The implementation was built on JAX Privacy and introduced optimizations for scalability:
- Vectorized per-example clipping for parallel efficiency
- Gradient accumulation to simulate large batches
- Truncated Poisson Subsampling integrated into the data loader for efficient on-the-fly sampling
The model achieved a formal DP guarantee of (ε ≤ 2.0, δ ≤ 1.1e−10) at the sequence level (1024 tokens).
How Do Scaling Laws Work for Private Training?
Training large models under DP constraints necessitates new scaling strategies. The VaultGemma team developed DP-specific scaling laws with three innovations:
- Optimal learning rate modeling using quadratic fits across training runs
- Parametric extrapolation of loss values to reduce reliance on intermediate checkpoints
- Semi-parametric fits to generalize across model size, training steps, and noise-batch ratios
This methodology enabled precise prediction of achievable loss and efficient resource use on the TPUv6e training cluster.
What Were the Training Configurations?
VaultGemma was trained on 2048 TPUv6e chips using GSPMD partitioning and MegaScale XLA compilation:
- Batch size: ~518K tokens
- Training iterations: 100,000
- Noise multiplier: 0.614
The achieved loss was within 1% of predictions from the DP scaling law, validating the approach.
How Does VaultGemma Perform Compared to Non-Private Models?
On academic benchmarks, VaultGemma trails its non-private counterparts but shows strong utility:
- ARC-C: 26.45 vs. 38.31 (Gemma-3 1B)
- PIQA: 68.0 vs. 70.51 (GPT-2 1.5B)
- TriviaQA (5-shot): 11.24 vs. 39.75 (Gemma-3 1B)
These results suggest that DP-trained models are currently comparable to non-private models from about five years ago. Importantly, memorization tests confirmed that no training data leakage was detectable in VaultGemma, unlike in non-private Gemma models.
Summary
In summary, VaultGemma 1B demonstrates that large-scale language models can be trained with rigorous differential privacy guarantees without compromising usability. While a utility gap remains compared to non-private counterparts, the release of both the model and its training methodology provides the community with a robust foundation for advancing private AI. This work signals a shift towards building models that are not only capable but also inherently safe, transparent, and privacy-preserving.
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