It's been over a year since large language models (LLMs) like ChatGPT were announced, and much has been learned. Perhaps the most valuable lesson is that the next generation of AI models will be smaller and use in-house data to make themselves relevant and useful. This move, however, will pressure solution builders to crystallize and streamline their generative AI strategy and be willing to run more applications internally while maintaining efficiency.
How is liquid cooling fueling the latest AI use cases?
As the hype of LLMs fades, organizations are changing their generative AI strategy to bring more services in-house, creating the need for high-density, liquid-cooled server environments. Popular tactics include:
- Targeting practical AI use cases like improving IT efficiency, AI support assistants, sales support, and automated marketing content creation
- Positioning LLMs as services to be combined with RAG implementations to harness business data
- Leveraging in-house, the latest, high-performing, liquid cooled servers to run smaller AI models targeted at solving business problems
- Utilizing solution builders to accelerate AI deployment and integration
The Myth of Large Homegrown AI Models
With the introduction of ChatGPT and other LLMs, 2023 started with AI proponents, including some solution builders, implying that organizations would create their own AI models. However, as everyone began to understand the complexity of such an undertaking, it became unrealistic, and solution builders then looked to leveraging existing models.
The Challenge of Adapting LLMs to Business Use Cases
LLMs aren't built in a way that lends them to solving out-of-the-box applications for business problems. After all, they primarily understand language and not the needs of specific industries or customers. This limitation presented a significant challenge for solution builders creating industry-specific AI applications.
The Answer: More Focused and Attainable Generative AI Solutions
The momentum for AI's application in the business world is strong. What makes today different is that companies, in collaboration with solution builders, consider business processes and challenges before implementing technology. This shift often entails redesigning organizations' internal processes. As a result, according to Dell Technologies Expert Travis Vigil, companies and solution builders are becoming more focused and practical in their planned use of AI, including:
- Improving IT efficiency
- Creating AI support assistants
- Sales support
- Creating and automating marketing content
Organizations will begin leveraging their in-house data at scale to make these and other generative AI solutions possible. Therefore, they're not relying solely on the capabilities of ChatGPT or other LLMs.
Instead, Matt Baker, Chief of AI Enablement at Dell Technologies, recommends creating an LLM-serving cloud that acts as a gateway between the LLM and applications that enterprises are developing internally. Solution builders can play a crucial role in designing and implementing this architecture, blending this computing to leverage an LLM's language capabilities against accurate, timely, and relevant data that organizations store and use daily.
Looking to the Future
As a result of the above trends, solution builders may be asked to build services for data scientists, which will likely be home-grown AI-infused applications with smaller AI models.
To feed these smaller, more agile models, organizations will use approaches like retrieval augmented generation (RAG) to harness proprietary data. The advantages of RAG include:
- Access to timely data via connecting to internal, up-to-date data sources
- Better accuracy by feeding LLMs with current factual data instead of questionable internet sources
- Contextual relevance and consistency thanks to grounding in data provided by organizations themselves
Effects on the Data Center
As organizations look to use more in-house resources to leverage smaller yet smarter AI applications, more will be asked of the data center. Compute density is already a topic on the minds of IT center leaders who want to house ever-powerful servers in the same amount of space.
It is a matter that often comes down to heat. Only so much air can be forced over warm components, and modern data centers are devoting as much as 30% of their power consumption to dissipating heat. Meanwhile, processors are getting hotter and hotter. For data center leaders and solution builders, the question becomes, how do you accommodate the needs of ever-sophisticated applications like AI when data centers are nearing the limits of their cooling capabilities?
Liquid Cooling for High Performing AI Servers
Solution builders are turning to liquid or immersion cooled servers to solve today's cooling dilemma. These machines submerge heat-generating components that dissipate heat more efficiently than their traditional, air-cooled counterparts.
The net result is that more servers can be deployed in any given space, making the density of liquid-cooled data centers up to ten times higher. This advantage enables organizations to deploy more higher-performing CPUs in the confines of their existing space.
At the same time, thanks to the fluids' inherent cooling properties, data center power consumption is reduced and refocused on computing resources rather than high-power fans, heat sinks, and air conditioning systems.
UNICOM Engineering - A Key Partner to Power and Cool Your AI Deployment
As your generative AI strategy moves to internal servers, they will demand more of your IT infrastructure. Look to UNICOM Engineering to design and integrate the best solutions to suit your computing and efficiency needs, often leveraging the latest processors and cooling technology.
As a Dell Technologies Titanium Partner and a titanium-level Intel Technology Provider, UNICOM Engineering assists in the complex, customized deployments that influence the development of future Dell Technologies products. UNICOM Engineering's capabilities extend far beyond equipment, designing and building effective computing solutions to accommodate your data center's current and future needs.