Sold by: Galaxys Cloud
Deployed on AWS
Pre-configured AMI with complete Deep Learning stack: Tesla T4 GPU, CUDA 13.0, PyTorch, TensorFlow, Jupyter Lab. Environment ready in 2 minutes for AI/ML projects. Product wherein additional charges apply for support provided by Galaxys.
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Overview
The Deep Learning Base GPU AMI is a pre-optimized, production-ready machine image specifically designed for artificial intelligence and machine learning workloads. Built on Ubuntu 24.04 LTS and fully configured with NVIDIA Tesla T4 GPU support, this AMI eliminates the complexity of environment setup, allowing data scientists, researchers, and developers to focus on what matters most building and deploying AI solutions. Product wherein additional charges apply for support provided by Galaxys.
Instant Productivity Zero Configuration Required: Launch and start coding within minutes no manual setup needed
Pre-optimized Environment: Everything from GPU drivers to deep learning frameworks is pre-installed and tested
Production-Ready Stack: Battle-tested configuration optimized for AI/ML workloads.
GPU Acceleration: NVIDIA Tesla T4: 16GB GDDR6 VRAM with 320 Tensor Cores for AI acceleration CUDA 13.0: Latest CUDA toolkit with full GPU computing capabilities Optimized Drivers: NVIDIA drivers 535.274.02 specifically tuned for deep learning workloads. Tensor Core Enabled: Automatic mixed-precision training for 2-3x performance boost.
Pre-installed and verified frameworks:
- PyTorch 2.0+ with CUDA support
- TensorFlow 2.15+ with GPU acceleration
- Jupyter Lab 4.0+ with notebook interface
- Scikit-learn for traditional ML
- OpenCV for computer vision
- Hugging Face Transformers ready
Data Science Ecosystem
- Python 3.12: Latest Python in isolated virtual environment
- Scientific Computing: NumPy, SciPy, Pandas, Matplotlib, Seaborn
- Image Processing: Pillow, OpenCV-Python
- Development Tools: Git, Docker, build-essential, debugging tools
Machine Learning & AI Development Deep learning model training and experimentation Neural network research and development Transformer models and large language models (LLMs) Reinforcement learning applications.
Pre-installed Software Stack Deep Learning Frameworks: PyTorch with CUDA support TensorFlow with GPU acceleration Keras API
Development Environment: Jupyter Lab 4.0+ Jupyter Notebook IPython kernels Code completion and debugging
Data Science Libraries: NumPy, SciPy, Pandas Matplotlib, Seaborn, Plotly Scikit-learn, XGBoost OpenCV, Pillow
System Tools: Docker container runtime Git version control System monitoring (htop, nvtop) Network utilities.
You can also deploy the following complementary products:
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PyTorch 2.1 with CUDA 12.1 - Optimized Deep Learning AMI https://aws.amazon.com/marketplace/pp/prodview-nbndtjeqywg32
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TensorFlow 2.15 with Keras 3.0 Deep Learning Stack https://aws.amazon.com/marketplace/pp/prodview-dnuw5pmugjrj6
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Deep Learning Base GPU AMI On Ubuntu 24.04 with Tesla T4 https://aws.amazon.com/marketplace/pp/prodview-6qacpepfhww7w
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Deep Learning OSS Nvidia Driver AMI GPU TensorFlow 2.13 https://aws.amazon.com/marketplace/pp/prodview-dd2v7zz5562zc
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Deep Learning OSS Nvidia Driver AMI GPU PyTorch 1.13.1 https://aws.amazon.com/marketplace/pp/prodview-52f2pzevpizue
Highlights
- Instant AI Development Environment Launch and start coding in under 2 minutes with a fully configured Deep Learning stack Tesla T4 GPU, CUDA 13.0, PyTorch, TensorFlow, and Jupyter Lab pre-installed and optimized. Eliminate days of environment setup and dependency conflicts.
- Production-Ready GPU Optimization Maximize your Tesla T4 performance with pre-tuned NVIDIA drivers, CUDA 13.0, and Tensor Core acceleration. Achieve 2-3x faster training with automatic mixed-precision and optimized memory management for deep learning workloads.
- Complete Data Science Stack Included Everything you need in one AMI: Python 3.12, Jupyter Lab, NumPy, Pandas, Scikit-learn, OpenCV, and monitoring tools. Perfect for computer vision, NLP, and ML projects from prototyping to production deployment.
Details
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Categories
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Features and programs
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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.xlarge Recommended | $2.40 |
g4dn.4xlarge | $2.40 |
g5.xlarge | $2.40 |
g5.24xlarge | $3.20 |
g5.12xlarge | $2.40 |
g4dn.8xlarge | $3.20 |
g5.48xlarge | $3.20 |
p3dn.24xlarge | $3.20 |
g5.16xlarge | $3.20 |
p3.2xlarge | $2.40 |
Vendor refund policy
For this offering, Galaxys Cloud does not offer refund, you may cancel at anytime.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Galaxys-ver2025
Additional details
Usage instructions
AWS Console Steps:
-
Go to EC2 Dashboard Launch Instance
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Search for: "Deep Learning Base GPU AMI - Ubuntu 24.04"
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Select instance type: g4dn.xlarge (recommended)
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Configure storage: 100GB (gp3 recommended)
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Launch with your existing key pair
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SSH Connection with Jupyter port forwarding ssh -i "your-key.pem" -L 8888:localhost:8888 ubuntu@YOUR_INSTANCE_IP
Access Complete Documentation & Guides Comprehensive User Resources For complete documentation, tutorials, and advanced usage guides, visit:
https://capture-galaxys.s3.us-east-1.amazonaws.com/DeepLearning.pdf
Support
Vendor support
Remote support seller@galaxys.cloud
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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Pre-configured AMI with complete Deep Learning stack: Tesla T4 GPU, CUDA 13.0, PyTorch, TensorFlow, Jupyter Lab. Environment ready in 2 minutes for AI/ML projects. Product wherein additional charges apply for support provided by Galaxys.
Customer reviews
reviewer2778747
Accelerated medical image models have improved training speed and streamlined delivery
Reviewed on Dec 09, 2025
Review from a verified AWS customer
What is our primary use case?
I am using Deep Learning Base GPU AMI on Ubuntu 24.04 with Tesla T4 for medical image recognition. The Tesla T4 GPU accelerates model training significantly. Ubuntu 24.04 is stable and easy to work with. Integration with AWS services is seamless. Overall, it is a great choice for deep learning tasks.
How has it helped my organization?
Deep Learning Base GPU AMI on Ubuntu 24.04 with Tesla T4 has greatly improved our organization by accelerating our model training times and increasing our productivity. The powerful Tesla T4 GPU enables us to process large datasets quickly, allowing us to develop and deploy models faster. This has enabled us to deliver results to our clients more efficiently and effectively.
What is most valuable?
The Tesla T4 GPU acceleration and Ubuntu 24.04 stability have been most valuable. The GPU acceleration enables fast model training, while the stable OS lets me focus on development, not environment configuration.
What needs improvement?
Improved compatibility with more frameworks, such as Keras or OpenCV, would be beneficial. Enhanced network configuration options would also be valuable. Support for additional instance types, like P3 or G5, could expand usability. More detailed documentation would help new users get started. Integration with AWS services like SageMaker could be tighter.
For how long have I used the solution?
I have used the solution for one year.
What's my experience with pricing, setup cost, and licensing?
The pricing for Deep Learning Base GPU AMI on Ubuntu 24.04 with Tesla T4 is competitive, considering the power and capabilities it offers. I would advise others to consider the cost-effectiveness of using this AMI, especially for large-scale projects or long-term use cases, as it can lead to significant savings compared to on-premises infrastructure.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)