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Deployed on AWS
This product has charges associated with it for seller support. Harness the power of NVIDIA GPUs the industry gold standard to power your LLM/Gen-AI/ML Projects with our multi-user Jupyter setup with pre-installed NVIDIA Drivers, AI libraries such as TensorFlow, PyTorch and lot more.
Overview
This is a repackaged open source software product wherein additional charges apply for support by TechLatest.net.
Important : For step by step guide on how to setup this vm , please refer to our Getting Started guide
Experience the future of AI/ML development with our NVIDIA GPU-accelerated virtual machine offer.
Key Features:
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CUDA Toolkit from NVIDIA which provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.
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NVIDIA GPU Acceleration: Unlock unparalleled computing power with our optimized NVIDIA GPU driver setup. Experience lightning-fast performance and accelerated computing for even the most complex AI/ML tasks, enabling you to train & inference LLM models faster and to handle larger datasets with ease.
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AI Extensions : Take your AI/ML projects to the next level with Jupyter AI extensions. Seamlessly integrate with over 100 pre-trained models like OpenAI/ChatGPT, GeminiOpenLLM & lot more to unlock new possibilities in LLM & generative AI.
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JupyterHub Collaboration: Collaborate effortlessly with colleagues and teammates using JupyterHub's multi-user environment. Share code, data, and insights in real-time, driving innovation and efficiency across your team.
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Comprehensive Library Support: Say goodbye to time consuming & tedious library installations - our virtual machine comes pre-configured with all the essential AI/ML libraries you need from TensorFlow to PyTorch and beyond, access the tools you love right out of the box.
Disclaimer: Other trademarks and trade names may be used in this document to refer to either the entities claiming the marks and/or names or their products and are the property of their respective owners. We disclaim proprietary interest in the marks and names of others.
Highlights
- NVIDIA GPU powered Jupyter VM with pre-installed NVIDIA Drivers & Python AI/ML Libraries
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 | $0.13 |
g3s.xlarge | $0.13 |
p3.2xlarge | $0.13 |
g3.16xlarge | $0.13 |
g5.16xlarge | $0.13 |
p2.8xlarge | $0.13 |
p2.xlarge | $0.13 |
g5.xlarge | $0.13 |
g4dn.4xlarge | $0.13 |
p3dn.24xlarge | $0.13 |
Dimensions summary
You pay by the hour for the software running on your chosen GPU instance. Each dimension maps to a specific AWS instance type across four families: G3, G4dn, G5, P2, P3, P4d, and P5. Pricing scales with instance size, so larger instances with more GPU capacity cost more per hour. You select the instance that fits your AI, ML, and LLM workload. There is no upfront commitment; billing tracks the hours you run. Every instance includes the same pre-configured software, so your choice depends on the compute power you need.
Top-of-mind questions for buyers
The hourly rate covers the pre-configured software running on your chosen GPU instance. Each instance comes with GPU drivers, common AI/ML libraries, a multi-user notebook environment, and integrations with pre-trained models. The software is identical across instances; only the underlying compute capacity changes with the instance type you select.
Software charges accrue only while the instance runs, billed per hour. A stopped or paused instance does not accrue software charges. Note that underlying AWS storage or reserved resources may still incur separate AWS fees, but the hourly software meter tracks running time only.
The instance type you choose drives the hourly cost. Instances differ by GPU count, memory, and compute size across the G3, G4dn, G5, P2, P3, P4d, and P5 families. Larger instances with more GPU capacity cost more per hour. The software fee is separate from and added to the AWS compute charge.
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Vendor refund policy
Will be charged for usage, can be canceled anytime and usage fee is non refundable.
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
Second release
Additional details
Usage instructions
Please visit https://techlatest.net/support/nvidia-aiml-support/aws_gettingstartedguide/index.html for step by step guide on how to provision and use this solution.
Resources
Vendor resources
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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