Streamline AI Deployment with Turnkey AI Software Solutions

Turnkey AI software solutions can significantly reduce the time and effort required to deploy AI models, often by up to 90%. This is because they come pre-configured with everything needed to get started.

With turnkey AI software, you can deploy AI models in a matter of hours, not weeks or months. This is a huge advantage for businesses that need to quickly adapt to changing market conditions.

Turnkey AI software solutions also provide a single platform for managing multiple AI models, making it easier to scale and maintain your AI infrastructure.

Hardware vendors like HPE, Cisco, Dell, and Lenovo are partnering with Nvidia to create AI data center solutions due to strong customer demand.

Nvidia dominates the AI market with its GPUs and software ecosystem, which includes Nvidia AI Enterprise, a software suite that makes it easier for enterprises to develop and deploy AI workloads.

The Nvidia AI Computing by HPE portfolio includes HPE Private Cloud AI, a fully integrated infrastructure stack that includes HPE ProLiant Servers, HPE GreenLake for File Storage, and Nvidia Spectrum-X Ethernet networking.

This solution is designed for AI inferencing and retrieval augmented generation (RAG) and will come in four configurations to support AI workloads of all sizes.

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Hardware vendors are clamoring to partner with Nvidia due to strong customer demand for AI workloads.

Nvidia dominates the AI market with its GPUs and software ecosystem, making it the go-to choice for many enterprises.

Several vendors, including HPE, Dell, and Lenovo, have already announced Nvidia-powered data center systems to meet this demand.

HPE has strengthened its AI offerings within its GreenLake platform through a partnership with Nvidia.

Private Cloud AI, a fully integrated infrastructure stack, is the key offering in HPE's new portfolio of products co-developed with Nvidia.

The solution will be available this fall as a fully managed or self-managed solution, supporting AI inferencing and retrieval augmented generation (RAG) and large language model training.

HPE Private Cloud AI will come in four configurations to support AI workloads of all sizes, from small Nvidia L40S GPU systems to larger systems running H100 NVL Tensor Core GPUs and Nvidia GH200 NVL2 Grace Hopper Superchips.

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Nvidia AI Enterprise, a software suite of AI tools, frameworks, and pre-trained models, will be featured in HPE Private Cloud AI, making it easier for enterprises to develop and deploy AI workloads.

The solution will also include HPE AI Essentials, a curated set of AI and data tools, and an embedded data lakehouse to unify and easily access structured and unstructured data stores.

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Nutanix GPT-in-a-Box is a comprehensive solution that brings together the necessary elements to deploy a large language model (LLM) into production. It includes Nutanix Cloud Platform Infrastructure on GPU-enabled server nodes, Nutanix Files and Object storage for running and tuning LLM models, an open-source software stack for deploying and running generative AI workloads, and support for a curated set of LLMs.

This solution is based on Nutanix's software-defined Nutanix Cloud Platform infrastructure, which accommodates GPU-enabled server nodes for seamless scalability of virtualized compute, storage, and networking. The platform also supports both conventional virtual machines and Kubernetes-managed containers.

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Nutanix GPT-in-a-Box offers a pre-trained solution designed for industrial safety applications, providing a demo system right out of the box. This allows developers to provide customers with a proof of concept right away.

The solution includes an open-source software stack, such as the PyTorch framework and KubeFlow MLOps platform, which empowers the deployment and management of AI workloads. This simplifies the AI integration process and optimizes IT expenditures through its cloud operating model, fostering cost-effective scalability.

Nutanix GPT-in-a-Box allows enterprises to retain exclusive ownership of AI content, aligning with stringent security, privacy, and compliance mandates. This is a key strength of the solution, as it accelerates the AI integration process without necessitating extensive retraining of existing teams or the acquisition of entirely new skill sets.

The solution's management interface, equipped with an enhanced terminal UI or standard CLI, further enhances the user experience. This makes it easier for developers to deploy and manage AI workloads, reducing the complexity and time required for AI development.

Here are some key features of Nutanix GPT-in-a-Box:

Nutanix's GPT-in-a-Box is a comprehensive AI solution that brings together the necessary elements to deploy large language models (LLMs) into production.

GPT-in-a-Box includes Nutanix Cloud Platform Infrastructure on GPU-enabled server nodes, which facilitates seamless scalability of virtualized compute, storage, and networking.

Nutanix Files and Object storage are also part of the solution, enabling the running and tuning of LLM models.

An open-source software stack is included for deploying and running generative AI workloads.

The solution supports a curated set of LLMs, including Llam2, Falcon, and MPT.

This multifaceted approach addresses the challenges of AI integration, allowing enterprises to retain exclusive ownership of AI content and align with stringent security, privacy, and compliance mandates.

The solution simplifies the process of building, fine-tuning, and executing models, including GPTs and LLMs, without requiring extensive retraining of existing teams or the acquisition of new skill sets.

Nutanix GPT-in-a-Box also offers a cloud operating model that fosters cost-effective scalability and accelerates the AI integration process.

Here's a summary of the key features of GPT-in-a-Box:

As a start-up or SME, you're likely looking for AI solutions that are easy to implement and don't break the bank. AI-as-a-Service offers turnkey solutions for exactly this purpose.

The concept of artificial intelligence was first introduced in the 1960s by John McCarthy, who described it as systems that can simulate human behavior. Initially, this involved rule-based AI, where human behaviors were converted into rules and manually programmed to automate tasks.

You can already achieve a great deal with AI without large budgets. Many cloud providers offer pre-trained models that you can connect via an API and start making predictions immediately.

For example, Salesforce's Einstein component allows you to use a food image model based on images of your own products. This can be a good way to test the value of a particular model before investing in custom training.

Off-the-shelf solutions like these are limited in flexibility, but they're a great way to get started with AI. You can also use platforms like Chatlayer, which develops AI-driven virtual assistants, or Robovision, which develops AI applications for various industries.

Here are some examples of off-the-shelf solutions and platforms:

These solutions can help you get started with AI and create value for your business, even with limited resources.