Sold by: RELEVANCE LABÂ
Deployed on AWS
This product has no additional software charges. You pay only for the Amazon EC2 instance and AWS infrastructure costs.
It provides a preconfigured Windows Server 2022 environment optimized for Generative AI and data science, featuring NVIDIA drivers, CUDA, Hugging Face, LangChain, RStudio, and JupyterLab. Designed for rapid development and integration with AWS Bedrock, SageMaker, and OpenSearch.
Overview
This product has no additional software charges. You pay only for the Amazon EC2 instance and AWS infrastructure costs.
Overview
The Windows Generative AI & Data Science Workspace by Relevance Labs is a repackaged Windows Server 2022 AMI designed for Generative AI, ML, and data science research. It comes with GPU acceleration and AWS-native integrations, providing a ready-to-use environment for AI experimentation and model development.
Key Features
- Remote Desktop Access: High-performance GUI via Amazon NICE DCV.
- Web & Utility Tools: Google Chrome, 7-Zip, Git, AWS CLI.
- GPU Ready: NVIDIA drivers, CUDA Toolkit, cuDNN for LLM training and inference.
- Development Environments: Visual Studio 2022, VS Code, PyCharm CE, JupyterLab, RStudio.
- Generative AI Frameworks: Hugging Face Transformers, LangChain, LlamaIndex, FAISS, LoRA/PEFT.
- Sample Workflows: RAG chatbot (Bedrock + OpenSearch), multimodal pipelines (Whisper/CLIP/LLaVA), MONAI for medical imaging.
- ML & Data Science Frameworks: PyTorch, TensorFlow, scikit-learn, PySpark, Dask, Vowpal Wabbit.
- Productivity Tools: LibreOffice, Docker, Anaconda.
- AWS-Native Integrations: Bedrock SDK, SageMaker SDK, OpenSearch connectors.
Technical Details
- Operating System: Windows Server 2022
- Remote Access: Amazon NICE DCV
- Browsers & Utilities: Google Chrome, Git, AWS CLI, 7-Zip
- Programming Languages: Python 3.x, R
- IDEs: Visual Studio 2022, VS Code, PyCharm CE, RStudio Desktop & Server
- Notebooks: Jupyter Notebook, JupyterLab
- GenAI & ML Frameworks: Hugging Face Transformers, LangChain, LlamaIndex, FAISS, PyTorch, TensorFlow, scikit-learn, PySpark, Dask, Vowpal Wabbit, MONAI, LoRA/PEFT
- Containerization: Anaconda, Docker, Docker Compose
- Office Tools: LibreOffice (Writer, Calc, Impress)
Highlights
- Preconfigured GenAI Research Environment on Windows Server 2022 GPU ready with NVIDIA drivers, CUDA, Hugging Face, LangChain, FAISS, and LoRA for LLM fine tuning.
- AWS Native Integrations Seamlessly connect with Bedrock, SageMaker, and OpenSearch for RAG chatbots, multimodal workflows, and scalable AI deployments.
- Developer Productivity Remote desktop via Amazon DCV, with preinstalled JupyterLab, RStudio, VS Code, and productivity tools for data science and enterprise research.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Win 2022
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
g4dn.8xlarge Recommended | $0.00 |
Vendor refund policy
NA
Legal
Vendor terms and conditions
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Content disclaimer
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
NA
Additional details
Usage instructions
Quick Usage Summary
- Subscribe to the AWS Marketplace product and launch an instance.
- Use at least 200 GB for the EBS Volume and select g4dn.4xlarge for smooth performance.
- AI and ML libraries are installed in the conda environment named "genai". To use them: a. Open Anaconda Prompt. b. Run: conda activate genai c. Run: python --version d. Import packages such as torch, transformers, langchain, faiss, scikit-learn, pyspark, dask, vowpalwabbit, monai, peft.
Connect via NICE DCV
- Open a browser and navigate to https://<your-public-dns-or-IP>:8443
- Log in using your Windows Administrator username and password.
- You will gain access to the Windows desktop in your browser. Note: Ensure that TCP port 8443 is allowed in the EC2 security group and Windows firewall.
Development IDEs
- Launch Visual Studio Code, Visual Studio 2022, or PyCharm from the Start Menu.
- Create new files or open existing projects.
- Suitable for Python, R, .NET, and full-stack development.
Anaconda
- Open Anaconda Navigator from the Start Menu to manage environments and packages.
- Alternatively, use Anaconda Prompt to run commands such as: conda list
JupyterLab and Python
- Access JupyterLab from the desktop or Start Menu.
- Run Python or R notebooks and import preinstalled libraries.
- Activate the genai environment for AI and ML workflows.
Machine Learning and AI Libraries
The environment includes preinstalled libraries for data science, machine learning, and generative AI. Examples:
- Transformers version 4.56.2
- LangChain version 0.3.27
- LlamaIndex version 0.14.3
- FAISS version 1.9.0
- PyTorch version 2.5.1 with CUDA 12.1 (GPU enabled)
- scikit-learn version 1.7.2
- PySpark version 4.0.1
- Dask version 2025.9.1
- VowpalWabbit version 9.10.0
- MONAI version 1.5.1
- PEFT version 0.17.1
GPU Support
- Verified with nvidia-smi: Tesla T4 GPU available
- PyTorch GPU acceleration is enabled
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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