GemmaX2-28-9B-v0.1 is a high-performance translation model designed to handle multilingual tasks across 28 languages. Built on top of the Gemma2-9B architecture, it is continually pretrained on 56 billion tokens of monolingual and parallel data and later fine-tuned using high-quality translation instructions. The model delivers reliable and fluent translations for widely spoken languages including English, Chinese, Spanish, Hindi, Arabic, French, and more. With a strong foundation in language understanding and generation, GemmaX2-28-9B-v0.1 serves as a practical tool for multilingual content translation, cross-cultural communication, and global-scale applications.
Model Resource
Hugging Face
Link: https://huggingface.co/ModelSpace/GemmaX2-28-9B-v0.1
✅ Minimum Configuration (For Inference with Quantization – 4-bit/8-bit)
- GPU:
- 1x NVIDIA RTX 3090 / RTX 4090 (24GB VRAM)
- Or NVIDIA A5000 / A6000 (48GB VRAM)
- Quantized model (using bitsandbytes or AutoGPTQ)
- vRAM:
- Minimum: 16GB
- Recommended: 24GB (for smoother inference)
- CPU:
- RAM:
- 32GB (minimum)
- 48GB (recommended for larger batch size)
- Disk:
- 50GB SSD (minimum)
- 100GB SSD (recommended to store model checkpoints, cache, and logs)
- Precision:
- Frameworks:
- PyTorch (>= 2.0)
- Transformers (>= 4.36.2)
🚀 Recommended Configuration (For FP16 Inference and Light Fine-Tuning)
- GPU:
- 1x NVIDIA A100 80GB or H100
- Or 2x RTX 6000 Ada (48GB x 2)
- vRAM:
- Minimum: 48GB
- Optimal: 80GB+ for full performance
- CPU:
- RAM:
- 64GB+ (to handle longer sequences and larger datasets)
- Disk:
- 200GB NVMe SSD (for dataset, logs, model weights)
- Precision:
- Parallelization:
- Model parallelism supported via Accelerate or FSDP
- Device map:
"auto" or "balanced" for multi-GPU setup
🧠 High-End Configuration (For Full Fine-Tuning & Large-Scale Inference)
- Setup:
- Multi-GPU cluster (Distributed Training)
- GPU:
- 4x H100 SXM 80GB
- Or 8x A100 40GB
- vRAM (combined):
- CPU:
- RAM:
- Disk:
- 1TB NVMe SSD (or more for long training runs)
- Parallelization Techniques:
- FSDP / DeepSpeed / ZeRO Offloading
- Gradient checkpointing
- Use Case:
- Best suited for enterprises or research labs fine-tuning the model on domain-specific data.
🧩 Additional Notes
- Model Size: ~10.2B parameters
- Tensor Type: bfloat16 (for optimal inference performance)
- Context Length: Supports long-form translation tasks
- Languages Supported: 28 (including English, Chinese, Hindi, French, Arabic, Spanish, Russian, Japanese, etc.)
Step-by-Step Process to Install GemmaX2 9B 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 H100s 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 A100X 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
Next, you will need to choose an image for your Virtual Machine. We will deploy GemmaX2 9B on a Jupyter Virtual Machine. This open-source platform will allow you to install and run the GemmaX2 9B on your GPU node. By running this Model on a Jupyter Notebook, we avoid using the terminal, simplifying the process and reducing the setup time. This allows you to configure the model in just a few steps and minutes.
Note: NodeShift provides multiple image template options, such as TensorFlow, PyTorch, NVIDIA CUDA, Deepo, Whisper ASR Webservice, and Jupyter Notebook. With these options, you don’t need to install additional libraries or packages to run Jupyter Notebook. You can start Jupyter Notebook in just a few simple clicks.
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 Jupyter Notebook
Once your GPU VM 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.
After clicking the ‘Connect’ button, you can view the Jupyter Notebook.
Now open Python 3(pykernel) Notebook.
Step 8: Update System
Run the following command to update the system:
sudo apt update && sudo apt upgrade -y
Step 9: Install PyTorch with CUDA
Run the following command to install PyTorch with CUDA:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
Step 10: Install Hugging Face Transformers
Run the following command to install hugging face transformers:
pip3 install transformers accelerate sentencepiece
Step 11: Download and Load the Model
Run the following code to download and load the model:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "ModelSpace/GemmaX2-28-9B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id).cuda()
Step 12: Run the Model
Execute the following code to run the model:
def translate(text, src_lang, tgt_lang):
prompt = f"Translate this from {src_lang} to {tgt_lang}:\n{src_lang}: {text}\n{tgt_lang}:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example Usage:
text = "Je t'aime"
src_lang = "French"
tgt_lang = "English"
translated_text = translate(text, src_lang, tgt_lang)
print(f"Translated Text: {translated_text}")
Conclusion
GemmaX2-28-9B-v0.1 stands out as a practical and efficient translation model built to handle multilingual tasks across 28 widely spoken languages. With its strong foundation in language understanding and generation, the model delivers accurate and fluent translations, making it ideal for content localization, cross-border communication, and global-scale applications. This guide walked through the complete setup of GemmaX2 9B on a GPU-powered virtual machine using NodeShift, ensuring smooth deployment, system compatibility, and fast execution. Whether you’re building multilingual tools or translating large-scale content, this model offers both performance and flexibility to integrate into your workflow with ease.
