Mark Heaps, VP of Brand and Creative at Groq, discusses challenges in scaling enterprise AI capabilities and highlights Groq's software-hardware ecosystem as a solution. They explore real-time AI systems, overcoming infrastructure challenges with kernelless systems, the two stages of model development, and understanding challenges in AI model development.
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Quick takeaways
Business leaders must consider infrastructure for real-time and fluid natural language processing.
Acquiring chips for on-premises deployments can cause delays and hinder rapid deployment.
Deep dives
The Challenges of Building Infrastructure for Enterprise AI
Business leaders face significant challenges when building the infrastructure required to scale enterprise AI capabilities. As the demand for real-time experiences grows, leaders must consider the need for infrastructure that can support real-time and fluid natural language processing for customers. The current Wild West stage of enterprise AI necessitates strategic thinking about infrastructure to serve evolving workloads and end-user expectations.
Supply Chain Challenges in Scaling AI Capabilities
One major obstacle faced by enterprises in scaling AI capabilities is the supply chain. Acquiring chips and hardware needed for on-premises deployments can take anywhere from 12 to 18 months, causing delays and hindering rapid deployment. This challenge has led many organizations to explore developing their own silicon and specialized processors to ensure timely access to the necessary hardware.
Challenges in Model Development and Deployment
Developing AI models and moving from training to deployment pose significant challenges. Model development often takes months, with an average of three to six months required to get a model up and running. The transition from development to deployment, known as inference, is where organizations can experience a return on investment. However, the rapidly evolving nature of AI models demands adaptability and scalability. Business leaders need to consider potential model changes, the need for reworking kernels, and the power draw associated with different models.
The Importance of Real-Time Inference and Rapid Deployment
For AI deployment, real-time inference plays a crucial role in meeting customer expectations and delivering value. Inference is the stage where models deliver insights and react to real-time data, requiring quick and accurate performance. Traditional systems can be clunky and struggle to provide real-time insights. However, deterministic systems, like Grok, offer synchronization between hardware and software, ensuring efficient performance and rapid adaptability to market changes. Real-time inference is essential to provide seamless user experiences and proactive business responses.
Today’s episode is the first in a special series we’re calling ‘Beyond GPU,’ taking a look at edge AI computing challenges and solutions with help from guests at leading vendors and superscale global tech brands leading the most advanced hardware platform teams on the planet. Today’s guest in the series is Mark Heaps, Vice President of Brand and Creative at Groq. Groq is a tech company specializing in simplifying computing challenges to accelerate workloads in artificial intelligence, machine learning, and high-performance computing. Mark joins Emerj Senior Editor Matthew DeMello on today’s show to talk about challenges facing business leaders in building the infrastructure necessary to scale enterprise AI capabilities. Throughout the episode, Mark examines the process across infrastructure, model development, and model deployment, offering compelling use cases and explanations for the technology behind them along the way. This episode is sponsored by Groq. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.
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