Generative AI (genAI) has created much hype and excitement for enterprises with its promises of new possibilities, from process automation and content creation to improved customer service and enhanced productivity. Almost every sector can benefit from genAI, yet many organizations struggle to embrace it. This is because their existing IT infrastructure cannot handle the computing requirements genAI demands nor remain sustainable in the process.
What can businesses do so they don’t miss out on the genAI promise? The first step to successful genAI setup and implementation is to understand what is needed for large language models (LLMs) and large-scale AI inferencing environments—foundations of genAI—to run smoothly and efficiently.
Building a strong foundation for genAI implementation
Every organization knows success can only be attained in the right environment; the same goes for technology. As innovations emerge, their requirements often scale. Particularly in the AI era where large computational power and storage capabilities are needed, it becomes necessary to revisit their existing infrastructure.
The good news is that in tandem with emerging innovations are solutions to help organizations bridge the gap. An example would be the Dell AI Factory. Organizations can get the infrastructure, solutions, and services tailored to their needs for a smooth and seamless AI and genAI deployment. Since every business has different needs, building an infrastructure that is the right fit will give them the foundation they need to scale up quickly as well. This means they can start small and build new AI capabilities as they continue to innovate and find new use cases. And given the powerful compute needed for genAI workloads, Dell Technologies collaborated deeply with NVIDIA to provide customers with the performance required to get started.
Enhancing servers for the genAI revolution
From the earliest microprocessors and rack-mounted servers to modern servers today that offer high-density computing, cloud integration, and scalability, we’ve seen technology progress over the years. However, the advent of genAI has created a new boom for GPU-server growth, driving the AI server market to reach $49.1 billion globally by 2027.
Even though, technically, traditional central processing units (CPUs) also can run LLMs, there are many limitations—speed being one of them. GPUs can perform technical calculations much faster and with greater energy efficiency compared to CPUs, which is why LLMs rely heavily on the former.
The Dell PowerEdge XE9680 is an instance where a server is built to support genAI workloads. Designed to handle demanding LLM training and large-scale inferencing environments, the Dell PowerEdge XE9680 features a factory-integrated rack-scale architecture, where partner components are seamlessly integrated for efficient and reliable deployment. It is a turnkey solution that comes with support and deployment services on hand for the fastest and most seamless implementation.
Its successor, the Dell PowerEdge XE9680L, now has a smart cooling feature for both CPUs and GPUs to allow for higher GPU density per rack as it maximizes compute power without overheating. It is optimized for NVIDIA HGX B200 to further accelerate computing and genAI.
Stay ahead of innovation with GPU-accelerated servers
To speed up genAI implementation and innovation, organizations should consider implementing GPU-accelerated servers that are purpose-built for AI applications. With rack server solutions like PowerEdge, businesses can focus on building their genAI workloads with full confidence that they have the appropriate infrastructure in place.
Replacing traditional servers with those designed for AI will ensure organizations remain competitive in today’s AI-driven landscape. More importantly, having the capabilities in place means that new ideas can quickly be made a reality, proving that genAI can indeed, live up to its hype.
To discover more about Dell Technologies genAI architecture solutions and what it can do for your organization, read: Innovate faster with GPU-accelerated AI.
