Deep Learning Base GPU AMI On Ubuntu 24.04 with Tesla T4
Galaxys Cloud · Software Development
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
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
Show the rest of the publisher’s description (56 more lines)
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:
- PyTorch 2.1 with CUDA 12.1 - Optimized Deep Learning AMI
https://aws.amazon.com/marketplace/pp/prodview-nbndtjeqywg32
- TensorFlow 2.15 with Keras 3.0 Deep Learning Stack
https://aws.amazon.com/marketplace/pp/prodview-dnuw5pmugjrj6
- Deep Learning Base GPU AMI On Ubuntu 24.04 with Tesla T4
https://aws.amazon.com/marketplace/pp/prodview-6qacpepfhww7w
- Deep Learning OSS Nvidia Driver AMI GPU TensorFlow 2.13
https://aws.amazon.com/marketplace/pp/prodview-dd2v7zz5562zc
- Deep Learning OSS Nvidia Driver AMI GPU PyTorch 1.13.1
https://aws.amazon.com/marketplace/pp/prodview-52f2pzevpizue
Highlights
Highlighted by the publisher on AWS Marketplace.
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.
Agent build and provenance
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The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Plans and pricing as listed
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Refund terms
As stated by the publisher on AWS Marketplace.
For this offering, Galaxys Cloud does not offer refund, you may cancel at anytime.
Sources
Linked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.