When companies began deploying AI infrastructure solutions nearly six years ago, they were innovating in AI exploration, cutting-edge research, and big science challenges.
Since then, many companies have focused their AI ambitions on more pragmatic use cases, including revolutionizing customer service, improving plant efficiency, delivering better clinical outcomes and minimizing risk. .
Today, we are witnessing the explosion of the greatest enterprise computing challenge of our time with the rise of natural language processing (NLP), which has become a critical capability for businesses around the world.
E-commerce giants use translation services for chatbots to support billions of users worldwide. Major manufacturers like Lockheed Martin use NLP to enable predictive maintenance by processing data entered by technicians, exposing clues in unstructured text that are precursors to equipment downtime.
Such efforts are happening all over the world. In Vietnam, for example, VinBrainAI is building clinical language models that allow radiologists to streamline their workflow and achieve up to 23% more accurate diagnoses through better synthesis and analysis of patient encounters.
What these organizations have in common is their desire to implement a large-scale AI infrastructure capable of training models to deliver incredible linguistic understanding with domain-specific vocabulary. The reality is that large language models, deep learning recommender systems, and computer graphs are examples of data center-sized problems that require infrastructure at a whole new scale.
To capitalize on this opportunity, more companies are setting up AI Centers of Excellence (CoEs), based on a shared IT infrastructure, that consolidate expertise, best practices and platform capabilities. to speed up problem solving.
The right architectural approach to an AI CoE can serve two critical modes of use:
- Shared infrastructure that serves large teams and any discrete projects developers may need to run on it
- A platform on which gigantic, monolithic workloads, such as large language models, can be developed and continuously iterated over time
The infrastructure supporting an AI CoE requires a massive compute footprint, but more importantly, it must be architected with the right network fabric and managed by a software layer that understands its topology, available resource profile, and network requirements. workloads presented to him.
The software layer is just as important as the compute-intensive hardware. It provides the underlying intelligence and orchestration capability that can enable a streamlined development workflow, quickly assign workloads to resources, and parallelize the most important issues across the platform. shape to get the fastest training cycle possible.
As AI CoE takes off in enterprises across all industries, many organizations are still looking to equip their business with AI and the infrastructure to make it happen. For the latter, new consumption approaches are gaining traction that combine supercomputing infrastructure with the businesses that need it, delivered in a hosted model, offered through colocation data centers.
IT leaders can learn more about these trends and how to develop an AI strategy by attending NVIDIA GTC, a virtual event running March 21-24 that features more than 900 sessions on AI. accelerated data centers and high performance computing.
NVIDIA’s Charlie Boyle, VP and GM of DGX Systems, will present a session titled “How Executive-Class AI Infrastructure Will Shape 2023 and Beyond: What IT Leaders Need to Know – S41821”. Join for free today.