To effectively leverage the advancements in Generative AI, many organizations are exploring solutions like HPE Private Cloud AI to simplify their technology stack and bridge the gap between complexity and their AI objectives.
Properly executed AI projects can be incredible productivity enhancers, solution enablers, and revenue generators. Yet many AI projects fail due to unnecessary complexity and/or lack of a practical use case.
Generative AI is a new technology, and its success depends upon many layers of optimized technology; often some parts of the solution being open-sourced. That’s why it’s important for organizations to consult early in the process with an expert to help set an achievable direction and practical expectations.
Allow IIS Technology to provide some proven guidance and apply effective guard rails for your next AI project. Read on to learn about:
- The differences between Perceptive AI (“traditional AI”) and Generative AI.
- The benefits of using a cloud-based development platform during use case selection and validation.
- Financial, performance, and security reasons for moving Generative AI workloads off the public cloud when the workloads are going into production (inference).
- Pre-engineered Generative AI solutions by HPE and NVIDIA ready for deployment from data centers to the edge.
- How the IIS workshop process can validate a Generative AI use case with an effective proof-of-concept (in-cloud or on-prem) with solutions like HPE Private Cloud AI to deploy production-ready AI inference safely behind company firewalls and close to data sources.
HPE and NVIDIA: The Partnership Powering HPE Private Cloud AI
Hewlett Packard Enterprise and NVIDIA, two long time leaders in the data center, have recently collaborated on a series of turn-key solutions that simplify the design and deployment of AI applications, especially Generative AI. HPE Private Cloud AI is purpose-built for Generative AI so you can get right to your data science and model tuning activities without worrying about the technology underpinnings that make Generative AI possible. The incorporation of NVIDIA’s AI Enterprise software and NVIDIA Inference Microservices (NIMs) have taken much of the complexity out of building a development Generative AI platform for you.
Simply getting a system ready to deploy an open-source model takes a lot of expertise – knowing which GPUs, servers, and software are right for your workloads, selecting the proper foundation model, building datasets and retrieval augmented generation (RAG) databases. It is a lot. And validating if your use case demands an investment in Generative AI infrastructure in the first place. The whole process requires the guidance of an experienced partner that knows the right questions to ask. That’s where IIS Technology adds value.
Perception-based AI vs. Generative AI: What’s the Difference?
Let’s establish a baseline understanding of older iterations of AI versus Generative AI, as that impacts everything from use cases to compute hardware to the type of output it generates.
Perception-based AI – the “traditional AI” that we’ve had for the last 15 years or so, is about making predictions based on past training. AI looks for patterns to perform tasks, like object or facial recognition, error and fraud detection, early-stage cancer detection, or predicting part failures. It can provide options for your next purchase given your shopping history or suggest the best route to your destination given traffic conditions. AI can scan computer code, make infinitely fast comparisons, and predict the weather. When properly trained, AI models make choices by comparing learned reference points to real-time data to spot trends and make decisions faster and more accurately than any human can.
Generative AI, or Gen AI, is the next generation of AI that can actually create new content in multiple formats (audio/video/text). Generative AI mimics human thought and creativity. Using “unstructured” data, it thinks, learns, and makes assumptions about the desired outcome – just by entering a few prompts of data on the subject at hand – or providing some files as reference.
Gen AI is not limited to text responses. For example, you can ask an AI platform to “paint a portrait of an alien world populated by creatures with four eyes and six legs.” A different AI engine could “compose a song about unrequited love in a pop vibe,” or “create an advertisement for product X featuring celebrity Y.” Ask an open-source AI tool to “write a 1,500-word blog on the intricacies of growing palm trees in the desert” and the results will be something that never existed before and is a unique document. Data is drawn from a multitude of sources, but the work is not a paraphrasing of existing documents, or a mixing of existing recorded audio.
The Generative AI Model Development Process
Traditional AI models are built on machine learning (ML) technology and make tactical decisions based on a given dataset. If a parameter changes, you must tell it. In contrast, Gen AI models have many more layers of complex technology and data processing. Using matrix math to evaluate a request by text or voice, it employs pre-trained and augmented data sets (RAG) to make “live” decisions to create a new piece of content that best meets the given parameters. Multiple GPUs, servers, software apps, drivers, and data libraries interconnect with the AI model to create a neural network. This enables the platform to think and question details that it does not yet understand so it can learn how to execute future requests without asking. Perception-based AI reads the map; Generative AI draws the map.
Generative AI applications such as chatbots, voice-based search engines, summarization tools, virtual assistants, and content creation tools are interactive and must be able to anticipate next steps and answer questions on the fly.
The development phase of an enterprise Gen AI project often starts in the Cloud. This is because most enterprises don’t have the resources to acquire and test the various combinations of hardware and software required to build a Generative AI platform in-house.
Since you haven't yet identified all the resources required to optimize your AI use case, why purchase yet? Leveraging the assets of a public cloud-based Gen AI service like Microsoft, AWS or GCP allows you to spin-up resources instantly and only pay for what you use – eliminating procurement delays and huge up-front investments in hardware before the “higher ups” have even signed off fully on the project. The cloud allows developers to test different AI training models and datasets, GPUs, servers, and software apps, essentially renting the compute and storage assets needed until your team has hammered out the details of your Gen AI project.
Move Generative AI Production Out of the Public Eye
So, you’ve chosen your appropriate AI model and have fine-trained it on your company’s unique use case and dataset. You’ve tested multiple combinations of NVIDIA GPUs, servers, storage, drivers and software on the public cloud to optimize performance and settled on the perfect configuration. Your Gen AI tool is running at peak efficiency and is ready for production. Why not just leave it running in the cloud?
Because public cloud-based Generative AI platforms are:
- Expensivestrong> - typically generating operating charges by metering the token output volume. As the system grows, the tokens grow exponentially.
- Less secure - as sensitive data is stored outside the business premises on untraceable resources.
- Too slow - supplemental data resources like RAG databases perform best when co-located near the data source of the Gen AI neural network.
Data sovereignty
By now it is assumed we all know that any data poured into a public, open-source Gen AI platform (ChatGPT, Perplexity.ai) is now out of your control. It becomes part of the knowledgebase the tool will use to learn and get smarter – and anyone can access it. Forever. The data doesn’t just vanish when you close the window. Your input is saved and could potentially be part of a future user’s output request.
I/O costs spiral out of control
Public Gen AI clouds are monetized by charging fractions of cent for each data input and output transaction – called tokens. Essentially, customers are charged for the number of times data is read from or written to cloud storage. During the testing and development stages it is palatable to pay the I/O charges on a small subset of data while the model is being evaluated and trained.
However, once it’s time to go into production and the tool goes live and I/O volumes multiply to match the scale of thousands of enterprise users adding more data every day, costs quickly skyrocket. You’ll end up paying the price of owning hardware on-premises many times over.
Migrate Generative AI Production to HPE Private Cloud AI
Therefore, once your Gen AI platform is ready for production it can be moved on-site or housed in a co-location facility where it can be safely walled off to process your data securely and minimize latency by proximity to data lakes or RAG datasets – without you being charged for every “keystroke.” If this sounds good, then you need an HPE Private Cloud AI.
Essentially, NVIDIA and HPE have removed the complexities of building a Generative AI platform through a series of pre-engineered solutions rightsized for Gen AI model training and inference workloads. You can focus on selecting Gen AI use cases for your industry from curated NVIDIA NIMs – which are Kubernetes containers that contain the open-source model and all needed dependencies so you can be confident in knowing you have the “whole kit” before you start building.
Leverage the power of NVIDIA-HPE Collaboration
Many enterprises planning Generative AI implementations wish to do so using NVIDIA’s industry-leading AI Enterprise Software and NVIDIA Inference Microservices (NIMs). Pre-optimized for various AI models and NVIDIA GPUs, NIMs are a set of microservices designed to optimize AI infrastructure for performance efficiency, cost-effectiveness, and streamline deployment of Generative AI models across various platforms, including clouds, data centers and workstations.
HPE Private Cloud AI systems feature an AI software layer designed in partnership with NVIDIA to seamlessly integrate with the NVIDIA AI Enterprise software stack and NIMs with HPE’s AI Factory software stack to fill out the entire solution. For organizations seeking to expedite Generative AI project development, HP Private Cloud AI is by far the best solution as it totally supports NVIDIA AI Enterprise software and incorporates NIMs into the platform for rapid deployment. When all these best-in-breed building blocks are integrated by IIS Technology, you get the best everyone has to offer.
Unlock the power of Generative AI on your budget
Purpose-built for AI, HPE Private Cloud AI is a turn-key, scalable, accelerated compute private cloud designed to power any AI project while ensuring data remains safely under enterprise control. Several pre-certified, pre-tested configurations are available in different sizes. All feature HPE ProLiant high-performance GPU servers, NVMe drives, AI toolkits, and HPE storage combined with NVIDIA NIMs and high-speed networking to unlock the power of Generative AI on your budget.
Designated by Developer, Extra-small, Small, Medium, Large, and Extra-large systems, all HPE Private Cloud AI configurations offer the HPE/NVIDIA stack, but each incrementally steps up in processing power and storage to handle increasingly large language models (LLM) and growing user counts. Choose the one that fits the complexity of your workload, user base, and foundation model.
The Small single rack configurations are ideal for basic LLM inference model training. The Medium configuration is also delivered as a single rack but offers more compute and storage capacity to support RAG augmentation for advanced query/response, content creation, and information retrieval applications. The Large multi-rack system provides more servers, storage, and networking power to enable AI model expansion, while the Extra-large configuration adds even more compute resources to tackle the most complex Generative AI models and the largest customer bases with millions of users pinging the platform every day.
HPE Private Cloud AI can be deployed on-premises, in co-location facilities, edge locations, or data centers. They arrive ready to use out-of-the-box, eliminating delays to implementation as well as the I/O fees and lack of control of public cloud-based Gen AI platforms.
Finalizing Your Generative AI Strategy with IIS and HPE Private Cloud AI
HPE Private Cloud AI can accelerate your Gen AI journey through pre-engineered private cloud solutions that work, eliminating platform configuration and ground-zero model training efforts so you can get right to work on your dataset and use case. But as we said up-front, getting to a validated use case comes first. And this requires the guidance and support of a world-class systems integrator like IIS Technology. Because most enterprises have a vision of what the end goal should be, but no experience with how to get there.
Our expertise in each step of the AI project lifecycle – data pipelining, orchestration, MLOps, model training, production, and retraining for continuous improvement – ensures we’ll deliver an HPE Private Cloud AI optimized for your enterprise and specific use case.
We’ll prove and define your Generative AI model in the public cloud with a test workload that we identify together in a planning workshop. If you need assistance choosing models we can assist with that as well. Then, when it’s time to deploy your neural network for inference (i.e. production), the appropriate HPE Private Cloud AI configuration can be selected to easily replicate identical – or enhanced – performance within your own private AI cloud.
Take the first step. IIS Technology offers a free workshop to validate your use case. If Generative AI is deemed to be a good fit for your goals, we can help define a pilot project and the resources to pull it all together to show a full proof-of-concept. We’ll invest in you, and we’ll all get smarter together.
Not sure if HPE Private Cloud AI is right for your enterprise and Gen AI use case? That’s why we’re here. Contact us now for a free, no obligation consultation to begin your Generative AI journey, or email us at info@iisl.com.
.jpg)
