When we talk about open language models, most discussions revolve around performance and scale. But what if the conversation centered on privacy first? That’s where VaultGemma comes in.
Developed by Google, VaultGemma is a unique variant of the Gemma family, built entirely from the ground up with Differential Privacy (DP) at its core. Using DP-SGD (Differentially Private Stochastic Gradient Descent), it provides strong, mathematically-backed guarantees that no single training example can be extracted from its parameters. In plain words: VaultGemma remembers patterns, not people.
Despite being lightweight (under 1B parameters), the model shows solid performance on reasoning, code, and natural language tasks, while ensuring that the privacy of its training data is never compromised. That makes it a rare model suitable for healthcare, finance, and sensitive communication systems—where both performance and privacy matter.
VaultGemma might not top the leaderboards compared to non-private models, but it represents a paradigm shift: proving that you don’t have to choose between utility and privacy—you can build responsibly from the start.
Benchmark Results
The model was evaluated on a range of standard academic benchmarks. As expected, there is a utility trade-off for the strong privacy guarantees offered by the model. The table below shows the performance of the 1B pre-trained (PT) VaultGemma model.
GPU Configuration Table for VaultGemma (1B)
VaultGemma is relatively small compared to today’s giants (like 70B+ models). Its <1B parameter size means it can run comfortably on consumer GPUs, laptops with good VRAM, and lightweight cloud setups. Here’s a breakdown:
| Scenario | Min VRAM | Comfortable VRAM | Example GPUs | Precision | Notes |
|---|
| Local experimentation (single prompt/chat) | 4 GB | 6–8 GB | RTX 3050, T4, Mac M2/M3 | FP16/BF16 | Works fine with Transformers pipeline or vLLM CPU+GPU hybrid offload. |
| Development & prototyping (small apps/chatbots) | 8 GB | 12–16 GB | RTX 3060, RTX 4060 Ti, A10G | BF16 | Smooth real-time interaction, batch size 1–2. |
| Scalable cloud deployment (chatbots, API serving) | 16 GB | 24 GB | RTX 4090, L4, A100 40G | BF16 | Best balance of throughput + low latency; easily serve multiple requests. |
| High-throughput workloads (batch inference, fine-tuning with DP) | 24 GB+ | 40 GB+ | A100 40G, H100, TPU v5e | FP32/BF16 | Needed for training/fine-tuning with differential privacy overhead. |
Pro Tip: If you’re just running VaultGemma for testing, even a mid-range consumer GPU (or CPU with enough RAM) will do. For fine-tuning with DP enabled, lean toward A100/H100 or TPU hardware, since DP-SGD adds extra compute overhead.
Resources
Link: https://huggingface.co/google/vaultgemma-1b
Step-by-Step Process to Install & Run Google VaultGemma-1B Locally
For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.
Step 1: Sign Up and Set Up a NodeShift Cloud Account
Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.
Follow the account setup process and provide the necessary details and information.
Step 2: Create a GPU Node (Virtual Machine)
GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H200s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.
Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deploy
Step 3: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 1 x RTX A6000 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements.
Step 4: Select Authentication Method
There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.
Step 5: Choose an Image
In our previous blogs, we used pre-built images from the Templates tab when creating a Virtual Machine. However, for running Google VaultGemma-1B, we need a more customized environment with full CUDA development capabilities. That’s why, in this case, we switched to the Custom Image tab and selected a specific Docker image that meets all runtime and compatibility requirements.
We chose the following image:
nvidia/cuda:12.1.1-devel-ubuntu22.04
This image is essential because it includes:
- Full CUDA toolkit (including
nvcc)
- Proper support for building and running GPU-based models like Google VaultGemma-1B.
- Compatibility with CUDA 12.1.1 required by certain model operations
Launch Mode
We selected:
Interactive shell server
This gives us SSH access and full control over terminal operations — perfect for installing dependencies, running benchmarks, and launching models like Google VaultGemma-1B.
Docker Repository Authentication
We left all fields empty here.
Since the Docker image is publicly available on Docker Hub, no login credentials are required.
Identification
nvidia/cuda:12.1.1-devel-ubuntu22.04
CUDA and cuDNN images from gitlab.com/nvidia/cuda. Devel version contains full cuda toolkit with nvcc.
This setup ensures that the Google VaultGemma-1B runs in a GPU-enabled environment with proper CUDA access and high compute performance.
After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.
Step 6: Virtual Machine Successfully Deployed
You will get visual confirmation that your node is up and running.
Step 7: Connect to GPUs using SSH
NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.
Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.
Now open your terminal and paste the proxy SSH IP or direct SSH IP.
Next, If you want to check the GPU details, run the command below:
nvidia-smi
Step 8: Install Python
Run the following command to install python:
sudo apt update && sudo apt -y install git curl build-essential python3.10-venv
Step 9: Install Pip, Wheel, Created and Activated Python 3.10 Virtual Environment
Run the following command to install pip, created and activated python 3.10 virtual environment:
python3.10 -m venv vaultgemma && source vaultgemma/bin/activate
python -m pip install --upgrade pip wheel
Step 10: Install PyTorch
Run the following command to install PyTorch:
pip install --index-url https://download.pytorch.org/whl/cu121 torch torchvision torchaudio
Step 11: Install Model Dependencies
Run the following command to install model dependencies:
pip install accelerate sentencepiece bitsandbytes
Step 12: Install Transformers
Why this step exists
- VaultGemma is new and declares a custom
model_type: "vaultgemma". Stable transformers (e.g., 4.56.x) doesn’t recognize it yet, so you get the KeyError: 'vaultgemma'.
- The latest dev build of
transformers includes the auto-mapping hooks + remote code loading needed to resolve that model type. That’s why we install from GitHub @main.
- We keep this step separate from the rest of the stack so you can upgrade/downgrade
transformers independently (without touching Torch, vLLM, etc.). If something breaks, you roll just this package back—clean and low-risk.
What we install and why
transformers (dev, from GitHub):
- Adds support for brand-new architectures like
vaultgemma.
- Ensures
AutoConfig/AutoModel can resolve classes when you pass trust_remote_code=True.
- Ships bug fixes ahead of the next PyPI release.
- We do not rebuild heavy deps here:
- Torch/CUDA are already installed and working—no need to touch them.
tokenizers stays on a prebuilt wheel (fast install, no Rust toolchain needed) that satisfies the version range required by dev transformers.huggingface_hub, accelerate are already present; no need to pin them unless you see specific errors.
Exact commands
Run the following command to install transformers:
# Install bleeding-edge Transformers (scoped to this venv)
pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@main"
# Verify
python - <<'PY'
import transformers, platform
print("Transformers:", transformers.__version__, "| Python:", platform.python_version())
PY
# Expect something like: 4.57.0.dev0
How this fixes your error
- With the dev build installed,
AutoConfig.from_pretrained(..., trust_remote_code=True) knows how to interpret model_type="vaultgemma" and import the repo’s custom modeling code. That removes the “Transformers does not recognize this architecture” failure.
Why it’s a dedicated step
- Isolation: Upgrading only
transformers avoids accidentally changing Torch/cuDNN/vLLM versions that affect GPU kernels or scheduling.
- Reproducibility: You can pin the exact git commit for blog readers or CI (
@<commit_sha>).
- Rollback-friendly: If a later change upstream causes a regression, you just reinstall a known-good commit of
transformers—no full env surgery.
Step 13: Install vLLM
Run the following command to install vLLM:
pip install "vllm>=0.5.5"
We install vLLM to serve VaultGemma as a fast, scalable, OpenAI-compatible API after first smoke-testing it with a simple Transformers script. Transformers is perfect for quick local runs (sanity checks, notebooks), but it isn’t optimized for high-throughput serving. vLLM adds production features: PagedAttention for efficient KV-cache memory use, better request scheduling for many concurrent users, streaming responses, easy /v1/chat/completions compatibility, simple flags for max context/batch size, and clean multi-GPU/TP scaling when you need it. In short—Option 1 (Transformers) proves the model works; Option 2 (vLLM) turns it into a reliable, low-latency API your apps can hit just like OpenAI.
Step 14: Install HuggingFace Hub CLI
Run the following command to install huggingface_hub[cli]:
pip install "huggingface_hub[cli]"
Step 15: Authenticate to Hugging Face Hub (paste your token)
- Create a token
- Log in from the VM (interactive – recommended)
# New command (the old `huggingface-cli login` is deprecated)
hf auth login
# paste your token when asked
hf whoami # quick sanity check
Step 16: Connect to Your GPU VM with a Code Editor
Before you start running model script with the Google VaultGemma-1B model, it’s a good idea to connect your GPU virtual machine (VM) to a code editor of your choice. This makes writing, editing, and running code much easier.
- You can use popular editors like VS Code, Cursor, or any other IDE that supports SSH remote connections.
- In this example, we’re using cursor code editor.
- Once connected, you’ll be able to browse files, edit scripts, and run commands directly on your remote server, just like working locally.
Why do this?
Connecting your VM to a code editor gives you a powerful, streamlined workflow for Python development, allowing you to easily manage your code, install dependencies, and experiment with large models.
Step 17: Create the Script
Create a file (ex: # app.py) and add the following code:
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, TextStreamer
import torch
model_id = "google/vaultgemma-1b"
# Tokenizer (remote code)
tok = AutoTokenizer.from_pretrained(
model_id,
use_fast=True,
trust_remote_code=True,
)
# Config first (forces remote mapping for model_type="vaultgemma")
cfg = AutoConfig.from_pretrained(
model_id,
trust_remote_code=True,
)
# Model
model = AutoModelForCausalLM.from_pretrained(
model_id,
config=cfg,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
prompt = "In two lines, explain what DP-SGD guarantees."
inputs = tok(prompt, return_tensors="pt").to(model.device)
streamer = TextStreamer(tok, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(
**inputs,
max_new_tokens=128,
temperature=0.7,
do_sample=True,
streamer=streamer
)
Facts to remember:
- Context window is 1,024 tokens—keep prompts + output within that. Hugging Face+1
- If you see a “model type … not supported” error, update
transformers to the latest; the HF thread shows such reports when the library is out of date.
What the script does:
- Import core Hugging Face classes (tokenizer, config, model) plus Torch and a live text streamer.
- Set
model_id to the gated repo google/vaultgemma-1b you authenticated for.
- Download & load the tokenizer from HF with
trust_remote_code=True so custom code in the repo is allowed.
- Download & load the model config first (also with
trust_remote_code=True) so the custom vaultgemma architecture is recognized.
- Download & load the actual weights as a causal LM, using BF16 for efficiency and
device_map="auto" to place them on your GPU.
- Define a short natural-language prompt asking for a two-line summary of DP-SGD.
- Tokenize the prompt into tensors and move them to the same device as the model.
- Create a
TextStreamer that prints generated tokens to stdout in real time (no prompts/special tokens).
- Call
model.generate(...) to produce text with up to 128 new tokens, sampling enabled (temperature=0.7, do_sample=True).
- Stream the model’s output to your terminal as it’s generated (no separate
print needed).
- Use default model max context (VaultGemma: 1,024 tokens) implicitly for prompt + output.
- Rely on your HF credentials/cache to fetch and reuse model files automatically.
- Keep everything self-contained in your Python venv so it doesn’t affect system packages.
Step 18: Run the Script
Run the script from the following command:
python3 app.py
This will download the model and generate response on terminal.
Step 19: Launch the OpenAI-compatible server (vLLM) and smoke-test it
- Start the server (keep this terminal open):
python3 -m vllm.entrypoints.openai.api_server \
--model google/vaultgemma-1b \
--dtype bfloat16 \
--max-model-len 1024 \
--gpu-memory-utilization 0.9 \
--port 8000
You’re ready when you see routes like /v1/completions, /v1/models printed in the log.
- Verify the server is up (new terminal):
curl http://127.0.0.1:8000/v1/models
# expect JSON listing: id = "google/vaultgemma-1b"
Do a quick completion (base model → use /v1/completions):
curl http://127.0.0.1:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "google/vaultgemma-1b",
"prompt": "Task: Write exactly two bullet lines about Differentially Private SGD (DP-SGD).\nOutput:\n- ",
"max_tokens": 80,
"temperature": 0.1,
"top_p": 0.9,
"stop": ["\n\n","END"]
}'
(Optional) Use it from apps
Set the OpenAI-compatible envs and point your clients at vLLM:
export OPENAI_BASE_URL="http://127.0.0.1:8000/v1/"
export OPENAI_API_KEY="sk-not-needed"
- Python client: use
openai and the /v1/completions endpoint.
- Streamlit UI: run the provided
vg_ui.py (or your app) and paste the same Base URL.
Note: vaultgemma-1b is pretrained (non-instruct). Prefer /v1/completions or launch vLLM with a --chat-template if you want /v1/chat/completions. Keep temperature low and constrain the output format for best results.
Conclusion
This guide walked through end-to-end setup: spinning up a GPU VM, installing deps, authenticating HF, smoke-testing with Transformers, then serving an OpenAI-compatible API via vLLM (plus optional Streamlit UI) and sizing GPUs. The takeaway: you can deploy a private-by-design LLM quickly and responsibly, then scale it or extend it with DP fine-tuning and safety guardrails as your needs grow.
