Sold by: 4PT Inc
Deployed on AWS
GPU-optimized AMI for ML/GenAI teams. Includes pre-configured (TensorFlow | PyTorch | CatBoost | XGBoost | TF-Keras | RAPIDS), intuitive dashboard, web console, multi-version Python, GPU metrics and secure controls (Admin/SSH). Supercharge workflows with built-in LLMs (ChatGPT | Claude | DeepSeek | Gemini | Grok) and customizable autonomous AI agents for code optimization & security audits.
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Overview
Ubuntu 22.04 + GPU + yAIT is a production-ready AWS AMI for AI and Machine Learning teams. It includes a modern web-based dashboard, pre-configured GPU-accelerated frameworks (TensorFlow, PyTorch, Keras, CatBoost, XGBoost and RAPIDS), multi-version Python support, an advanced web console for executing workloads on AWS GPUs, and real-time monitoring with detailed performance analytics.
Built-in Generative AI capabilities provide native access to leading LLMs, including DeepSeek, ChatGPT, Claude, Gemini and Grok, directly from the platform. Users can create customizable autonomous AI agents for code optimization, security auditing and performance analysis, while enterprise-grade multi-tenant controls ensure secure administration, role-based access and SSH management with password or .PEM auth.
Focus on building, not configuring. Accelerate AI development with a production-ready platform, flexible support options and full licensing compliance across all integrated frameworks and LLMs, maximizing productivity, scalability and GPU ROI from day one.
Highlights
- CORE INFRASTRUCTURE & GPU-ACCELERATED ML FRAMEWORKS: Ubuntu 22.04 + NVIDIA CUDA ready | AWS AMI with preinstalled AI/ML stack | CatBoost, XGBoost, RAPIDS, Keras, PyTorch, TensorFlow | Python 3.9, 3.10 & 3.11 support | Modern web GUI & interactive console | Native execution on AWS GPU instances | Real-time monitoring and advanced GPU analytics | Secure, scalable, production-ready environment
- GENERATIVE AI WORKSPACE WITH NATIVE LLM INTEGRATION: Built-in access to DeepSeek, ChatGPT, Claude, Grok & Gemini | No external web pages required | Fully customizable autonomous AI agents | Settings-driven configuration and management | Real-time code optimization & security audits | Automated performance recommendations | Multi-tenant security architecture | Separate Admin and User dashboards | Secure SSH access with password and .PEM auth
- PROFESSIONAL SUPPORT, DEPLOYMENT VELOCITY & LICENSE COMPLIANCE: Expert technical support and compliance guidance | Production-ready deployment with zero setup friction | Faster AI model development and cloud ROI | Optional professional services by 4PT Inc. | Flexible plans for startups, SMBs and enterprises | Basic, Standard, Premium and Custom support tiers | Preserves original tool functionality | Full compliance with open-source and proprietary licenses
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04
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.xlarge Recommended | $19.27 |
g5.4xlarge | $19.27 |
g6.2xlarge | $19.27 |
g5.2xlarge | $19.27 |
p5e.48xlarge | $19.27 |
p4d.24xlarge | $19.27 |
p5.48xlarge | $19.27 |
g4dn.metal | $19.27 |
g6e.4xlarge | $19.27 |
g4dn.16xlarge | $19.27 |
Dimensions summary
You pay by the hour for the GPU instance type you run. Each dimension maps to a specific AWS GPU instance, so pricing scales with the hardware you choose. Options span four instance families: G4dn, G5, G6, and G6e for general GPU workloads, plus P4d, P5, P5e, and P5en for larger compute needs. Within each family, sizes range from xlarge up to 48xlarge or metal. Larger sizes carry more GPU, memory, and CPU capacity, so their hourly rate is higher. You are billed only for the hours each instance runs.
Top-of-mind questions for buyers
Each dimension maps to one AWS GPU instance running for one hour. You get a virtual machine sized to that instance type, preloaded with the vendor's GPU-optimized image for AI, security, and automation tasks. Larger sizes include more GPU, memory, and CPU capacity.
Software charges meter running hours only. A stopped or powered-off instance does not accrue the hourly software fee. Note that underlying AWS storage and other resources tied to a stopped instance may still incur separate AWS charges.
Cost changes with the instance type you run, not automatically. You choose which instance to launch. A larger size within a family carries more GPU, memory, and CPU, so its hourly rate is higher. You are billed at the rate for whichever instance is actually running.
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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
User Metrics & Multi-Tenant Management
- Displays the total number of users registered on the system
- Shows the number of currently active users in real time
- Provides distinct access role differentiation for Dashboard Admins and Users
- Features secure remote SSH access management via passwords or cryptographic .PEM files
Python Versions
- Shows all three installed versions: 3.9.18, 3.10.12 and 3.11.9
- The default version selected by the Admin is clearly marked
- Includes instant workspace switching capabilities across all three environments
AI Tools & Core Frameworks
- A list of all pre-installed AI tools is displayed (with versions noted where applicable)
- Features pre-configured environments for TensorFlow, PyTorch, Keras (TensorFlow), includes full out-of-the-box support for CatBoost, XGBoost and RAPIDS.
- Includes a web-based console to execute Python code or machine learning scripts directly on real AWS GPU hardware
- Strictly adheres to upstream open-source or proprietary tool licensing compliance
Next-Gen GenAI Workspace & Autonomous Agents
- Native API configuration panel for leading LLM providers
- Direct, built-in interactive chat with DeepSeek, ChatGPT, Claude, Grok and Gemini
- Eliminates the need to access or rely on external web pages for AI assistance
- Features fully customizable autonomous AI agents configured directly from the settings panel
- Delivers real-time automated code optimization, security audits and performance suggestions
Advanced GPU Information Panel
- Displays manufacturer (e.g., NVIDIA) and driver/CUDA compatibility
- Shows exact model (e.g., A100, H100, H200, V100, T4) running on real AWS hardware
- Tracks GPU Usage, RAM usage, Temperature (in Celsius) and Fan Speed in real time
- Provides deep statistical usage metrics and analytics for historical performance reporting
System Information Panel
- CPU usage, Virtual memory usage, Swap memory and Disk usage tracking
- Helpful for diagnosing resource availability and infrastructure bottlenecks
- Built on a secure Ubuntu 22.04 base to maximize cloud infrastructure investment
Additional details
Usage instructions
- Ensure Port 22 is Open
- In the AWS Management Console:
- Go to EC2 > Security Groups
- Select the security group attached to your instance
- Under the Inbound rules tab, add a rule to allow TCP traffic on port 22 (SSH) from your desired source (e.g., 0.0.0.0/0 for public access or your IP for restricted access)
- In the AWS Management Console:
- Locate your .PEM key file
- This file is generated and downloaded from the AWS Console when launching the instance (e.g., my-key.pem)
- Set the correct permissions on your .pem file
- Run the following command to restrict access:
- sudo chmod 400 my-key.pem
- Run the following command to restrict access:
- Connect to your EC2 instance via SSH (port 22)
- Use the following command (replace PUBLIC_IP with your instance's actual public IP address):
- ssh -i my-key.pem ubuntu@PUBLIC_IP
- Use the following command (replace PUBLIC_IP with your instance's actual public IP address):
- Accept the Terms and Conditions
- On first login only, the system will display the Terms and Conditions of Use:
- Press A to Accept: credentials will be automatically generated for dashboard access
- Press C or Ctrl+C to Cancel: your session will be terminated and access will be denied until you accept the terms
- On first login only, the system will display the Terms and Conditions of Use:
- Access the Web Dashboard
- Once the terms are accepted, you can access to yAIT in your browser using:
- http://PUBLIC_IP:2412
- Once the terms are accepted, you can access to yAIT in your browser using:
Support
Vendor support
For support related to "yAIT ," customers can reach out via email at 4support@4pertech.com . When you purchase "yAIT," you gain access to professional technical assistance provided by our expert support team. We offer responsive, personalized help to ensure smooth deployment and optimal use of the AMI. Support covers setup guidance, performance optimization, and issue resolution. Please note that support services are provided at an additional cost, and detailed service plans are available upon request to match your specific needs.
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.
Customer reviews
reviewer2764809
Has significantly accelerated AI model deployment and improved analysis workflows
Reviewed on Oct 13, 2025
Review from a verified AWS customer
What is our primary use case?
My primary use is to run artificial intelligence models with GPU, taking full advantage of the power for fast processing in my analysis and prediction projects.
How has it helped my organization?
It has greatly improved our organization because it has allowed us to significantly speed up the development and deployment time of models, reducing costs and improving the quality of the results.
What is most valuable?
The features I value most are the direct integration with AWS Marketplace, efficient GPU resource management, and the ease of scaling based on demand. This saves us a lot and reduces headaches with infrastructure.
What needs improvement?
I think it could improve in documentation, making it more detailed for new users and providing some kind of support or tutorials in Spanish. It would be good if the next version included real-time GPU usage monitoring and proactive alerts to optimize costs and avoid bottlenecks.
For how long have I used the solution?
I have used it for approximately one and a half weeks.
Which solution did I use previously and why did I switch?
We previously used other local GPU solutions, but we switched because of the stability, support, and the easy integration with AWS, which makes administration and deployment much easier for us.
What's my experience with pricing, setup cost, and licensing?
Regarding pricing, I would recommend that each company clearly define their usage and size their GPU instances to pay only for what they really use, as the scalability is flexible and helps keep costs under control.
Which other solutions did I evaluate?
Of course, we evaluated other options before choosing, such as Google Cloud and Azure products, but its compatibility and price on AWS Marketplace offered us the best quality-price ratio for our needs.
What other advice do I have?
I advise taking advantage of the AWS integration and the ease this product offers to accelerate your AI projects.
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)