Private clouds are emerging as essential to deploying AI solutions. Discover why they’re integral to enterprise AI strategies and explore a transformative solution — HPE Private Cloud AI, part of the NVIDIA AI Computing by HPE portfolio.
By Brian Gruttadauria, Chief Technologist Hybrid Cloud, Hewlett Packard Enterprise
In the dynamic landscape of modern technology, artificial intelligence (AI) stands as a transformative force, poised to revolutionize various industries. However, the journey to harnessing AI's full potential is complex, particularly for enterprises just embarking on this path.
To navigate this journey successfully, enterprises must adopt a cloud approach, with private clouds emerging as essential for deploying AI solutions. A private cloud addresses latency concerns when working with a public cloud. It also offers the security, performance, and customization needed for effective AI implementation, making it an attractive option for organizations with stringent requirements and heavy AI workloads.
This blog explores the value proposition of private clouds in AI, emphasizing their critical role in addressing enterprise needs.
The state of AI in enterprises
Most enterprises today are just beginning their AI journey. While the promise of AI is widely recognized, achieving its benefits requires overcoming significant challenges. Key among these is the need for a robust and flexible cloud infrastructure to support diverse and evolving AI applications.
The case for private clouds in AI deployment
Private clouds offer a compelling solution for enterprises aiming to deploy AI effectively. Here are the core reasons why private clouds are integral to enterprise AI strategies:
1. Data proximity and latency: AI applications often require real-time processing of vast amounts of data. For example, autonomous vehicles generate terabytes of data daily, necessitating immediate processing to make split-second driving decisions. Private clouds, by keeping data close to the processing units, minimize latency and ensure timely data processing.
2. Enterprise governance, regulatory compliance, and data sovereignty: Different jurisdictions have varying regulations regarding data privacy and sovereignty, such as GDPR. Private clouds enable enterprises to meet these regulatory requirements effectively, ensuring compliance and protecting sensitive data. This is especially important as enterprises work to improve the accuracy of generative AI systems using techniques like retrieval augmented generation (RAG). By using RAG-based deployment architictures, enterprises are able to use their own proprietary data, to provide more accurate and up-to-date responses, reduce ambiguity, and build trust by giving models sources they are allowed to cite.
3. Cost, predictability and flexibility: Private clouds offer predictable and flexible cost models, such as subscriptions or fixed costs, which are advantageous for budgeting and financial planning. A comparative analysis over three years shows that the cost of running high-performance GPU servers on-premises in private clouds is significantly lower ($194,444) than equivalent public cloud services ($324,087). This cost efficiency makes private clouds a financially viable option for enterprises.
4. Diverse computational resources: AI workloads demand a variety of computational resources, with purpose-built AI infrastructure designed for optimal performance at scale. Private clouds can be tailored to provide the necessary computational power and accelerated infrastructure, ensuring that generative AI foundation models can be customized with a business’s proprietary data as well as deployed efficiently and effectively.
A first-of-its-kind turnkey private cloud for AI
One of the most transformative solutions at the forefront of this revolution is HPE Private Cloud AI , part of the NVIDIA AI Computing by HPE portfolio. Let's explore how instant AI productivity can reshape your approach to AI development and data management, ensuring enterprise-grade confidence and control while maintaining the flexibility of a cloud experience.
The following diagram gives an overview of the HPE Private Cloud AI architecture. All the AI software from NVIDIA and HPE is integrated with accelerated AI infrastructure from NVIDIA and HPE to deliver an optimized time to value for your enterprise AI projects. HPE Private Cloud AI simplifies every step of the way, from the out-of-the-box experience, through AI experimentation, to the enterprise operations of the delivered solution. All delivered securely to meet the most demanding compliance requirements for generative AI workloads in the enterprise.
The foundation of the software stack starts with NVIDIA AI Enterprise, which includes NVIDIA NIM inference microservices. NVIDIA AI Enterprise accelerates data science pipelines and streamlines development and deployment of production-grade copilots and other GenAI applications. NVIDIA NIM delivers easy-to-use microservices for optimized AI models and workflows — offering a smooth transition from prototype to secure production deployment of AI models in a variety of use cases.
Complementing NVIDIA AI Enterprise and NVIDIA NIM, HPE AI Essentials software delivers a ready-to-run set of curated AI and data foundation tools with a unified control plane. With self-serve access to essential AI tools, developers can boost their productivity by up to 90%. This leap in efficiency allows for quicker iteration cycles, faster deployment of solutions, and more time for creative problem-solving. Imagine a world where developers spend less time on repetitive tasks and more time on innovation — that’s the power of AI-enhanced productivity.
HPE meets customer needs with flexible solutions
AI applications and enterprise requirements are continually evolving. Private clouds offer the flexibility needed to adapt to these changes, providing scalable solutions that can be adjusted to meet specific business needs. This adaptability is crucial for enterprises aiming to stay competitive and innovate continuously in their AI endeavors.
HPE co-developed the HPE Private Cloud AI solution with NVIDIA to meet a wide range of enterprise generative AI use cases — from horizontal applications like chatbots and code generation, to industry-specific applications like fraud detection and drug discovery. And with the integrated HPE Private Cloud AI data fabric, customers have seamless access to all their enterprise data anywhere.
HPE Private Cloud AI delivers a fully integrated AI infrastructure stack that includes NVIDIA Spectrum-X Ethernet networking, HPE GreenLake for File Storage, and HPE ProLiant servers with support for NVIDIA L40S, NVIDIA H100 NVL Tensor Core GPUs and the NVIDIA GH200 NVL2 platform. Four different NVIDIA-Certified System configurations are available (small, medium, large, and extra large) to scale across a broad range of inferencing, RAG, and fine-tuning workloads in the enterprise.
HPE Private Cloud AI simplifies monitoring and maintenance
One of the core challenges of Day 2 operations is monitoring the performance of deployed AI applications and models. HPE Private Cloud AI provides comprehensive monitoring through OpsRamp, a Hewlett Packard Enterprise company, to track key performance metrics, detect anomalies, and alerts operators to potential issues. These tools offer real-time insights into model behavior, allowing for proactive maintenance and swift resolution of problems. By automating much of the monitoring process, HPE Private Cloud AI reduces the manual effort required, freeing up valuable resources for other tasks.
OpsRamp now provides observability for the end-to-end NVIDIA accelerated computing stack, including NVIDIA NIM and AI software, NVIDIA Tensor Core GPUs, and AI clusters as well as NVIDIA Quantum InfiniBand and NVIDIA Spectrum Ethernet switches. IT administrators can gain insights to identify anomalies and monitor their AI infrastructure and workloads across hybrid, multi-cloud environments.
The new OpsRamp operations copilot utilizes NVIDIA’s accelerated computing platform to analyze large datasets for insights with a conversational assistant, boosting productivity for operations management.
Enhanced security
Security is crucial for AI applications and the enterprise data they access. In recent events, there have been incidents where models downloaded from open-source sites were compromised, leading to exploitation of critical enterprise data. To combat this, HPE has partnered with CrowdStrike to provide a solution that neutralizes these threats. They have integrated the CrowdStrike API into OpsRamp, offering an end-to-end operational view of endpoint security to protect your AI application services and private cloud infrastructure.
Bringing it all together
HPE Private Cloud AI brings together AI-native compute, storage, and networking infrastructure managed through HPE GreenLake cloud.
HPE Private Cloud AI provides customers with a “one-click,” ready-to-run, full-stack AI solution leveraging NVIDIA AI accelerators, networking, and software with HPE’s AI storage, compute, and the HPE GreenLake cloud.
Conclusion
The new HPE Private Cloud AI solution co-developed with NVIDIA provides a fast and flexible path for developing and deploying generative AI applications across a broad range of AI workloads and use cases.
As enterprises embark on their generative AI journeys, embracing a cloud approach is not just beneficial but essential. Private clouds stand out as a strategic choice, offering the necessary infrastructure, compliance, cost efficiency, and flexibility required for successful AI deployment. By leveraging private clouds, enterprises can overcome the challenges associated with AI implementation and unlock the transformative potential of artificial intelligence.
In a world where data proximity, regulatory compliance, and computational demands are paramount, private clouds provide a robust foundation for enterprises to build, deploy, and scale their AI solutions. As the AI landscape continues to evolve, private clouds will remain a critical enabler of enterprise success, driving innovation and efficiency in the digital age.