In-house developed applications will play a critical part in every AI journey. They support customizations for your business’s requirements, enabling you to gain a significant advantage over competitors using generic tools. They also remove dependencies on third-party application vendors, making integration easier and reducing non-compliance risks. However, they also come with challenges in developing the sandbox infrastructures for your AI application development, testing and experimentation phases.
Companies are developing proprietary AI applications that focus on analysing data and Generative AI (GenAI) applications that concentrate on producing new content. In this second blog post in our series, we will describe what you need to build your applications and the best architectures to realise the value of your AI investment.
The sandbox challenge
To develop your AI and GenAI applications efficiently, you need high-quality AI sandbox infrastructures with varying processing and storage resources that allow you to test, learn, break, and refine your applications. Your GenAI sandbox will need to support large language models, while your AI sandbox will need to support a broader range of AI models and technologies. Nevertheless, you’ll need to dedicate significant resources to the sandboxes’ construction and management. And you may find they’re both complex and resource-intensive to administer, supporting multiple tools, software libraries and interfaces that developers need to use.
Taking a partnered approach to accelerate success
Many companies find it best to take a truly partnered approach to building sandbox infrastructures. By working with solution providers with AI platforms, expertise and partnerships to support your AI journey — including development — you can transform your business with AI faster and at a lower cost. The potential savings are enormous. You avoid capital expenditure on hardware, operational expenses for maintaining the infrastructure and potential issues from a lack of experience. Furthermore, you gain the flexibility to scale your sandbox infrastructure quickly and cost-effectively up or down as your needs change.
Bring AI to your data using best-of-breed solutions
Our goal at Dell Technologies is to support you end-to-end in your AI journey, accelerating the development of AI applications that will transform your business’s performance. Our Dell AI Factory with NVIDIA brings AI to your data, including the sandbox infrastructures you need to test, learn, break, and refine the AI solutions that can give you an edge in the modern business world.
When you use the Dell AI Factory with NVIDIA, you gain access to simplified, tailored and trusted turnkey platforms with expert services to achieve AI outcomes faster. It will help you streamline and accelerate your AI journey, create better outcomes for your business, and protect and sustain your success through control that enhances your security and efficiency.
Powered by the broad Dell Technologies portfolio of AI infrastructure with industry-leading accelerated computing processors, the Dell AI Factory with NVIDIA allows you to bring AI to your most valuable data. It offers the world’s broadest GenAI solutions portfolio from desktop to data center to cloud, all in one place. Inferencing— the process by which a trained AI model applies its learned knowledge to new, unseen data to make predictions, draw conclusions or solve tasks — is 75% more cost-effective than public cloud Infrastructure-as-a-Service.[1]
What’s more, with the robust Dell Technologies partner ecosystem to support your AI initiatives, you’re not wedded to one model, framework or operating environment. You can choose the ones that are right for your business.
Discover more about our AI story and learn how the Dell AI Factory with NVIDIA can advance your AI strategy.
[1] Based on Enterprise Strategy Group research commissioned by Dell, comparing on-premises Dell infrastructure versus native public cloud infrastructure as a service, April 2024. Analysed models show a 7B parameter LLM leveraging RAG for an organization of 5,000 users being up to 38% more cost-effective and a 70B parameter LLM leveraging RAG for an organization of 50,000 users being up to 75% more cost effective. Actual results may vary.
