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Zadara 2025 Predictions: 2025 Forecast - The Expansion of AI at the Edge

vmblog-predictions-2025 

Industry executives and experts share their predictions for 2025.  Read them in this 17th annual VMblog.com series exclusive.

By Yoram Novick, CEO, Zadara

Advancements in artificial intelligence are reshaping the tech world, particularly through the rise of AI at the edge. This transition is being fueled by a growing demand for real-time, context-aware responses across various industries, supported by breakthroughs in AI inference technology. With the need for agile, localized AI solutions, the coming year is likely to witness a surge in infrastructures designed for edge-based AI. These systems will focus on performing inference locally, for speed and efficiency.

One key factor propelling this change is the rapid advancement in small language models (LLMs). These efficient models support high-quality AI inference on devices with limited computing power. While larger LLMs still provide better user experience, its these smaller models that are now achieving performance levels sufficient for most practical applications. And it's this progress that enables organizations to deploy advanced AI capabilities at the edge, reducing the reliance on cloud-based processing while maintaining functionality. 

The push for edge AI inference is largely driven by the need for low-latency, privacy-centric applications across both consumer and enterprise markets. Traditional cloud-based AI offers robust computational power but often falls short in terms of latency and data transfer expenses. Edge-based solutions bring AI closer to the data and/or user, delivering faster and contextually relevant responses. For instance, healthcare applications that manage sensitive patient information benefit significantly from on-device AI, which ensures compliance with privacy laws while maintaining efficiency. Beyond latency concerns and compliance, these deployments provide other advantages, such as reduced reliance on network bandwidth. 

Edge-based AI is tailored for specific applications, spanning natural language processing, image analysis, predictive maintenance, and real-time decision-making. In the automotive sector, for example, AI-powered edge devices enable autonomous vehicles to process sensor data locally, allowing for split-second decision-making independent of cloud connectivity. Similarly, manufacturing facilities are leveraging AI for real-time quality control, instantly identifying defects to minimize waste and downtime. The adoption of 5G technology further strengthens the appeal of edge solutions by enhancing device connectivity and enabling seamless communication with centralized systems. 

This trend extends beyond performance advancements, emphasizing improvements in user experience and operational efficiency. These capabilities also translate to real-time personalization in consumer applications. In this context, edge AI aligns with the growing demand for adaptable, cost-effective technologies that deliver low-latency responses in dynamic environments. 

Recently, the focus in AI has been on training large models, which requires substantial computational resources and cloud infrastructure. However, as AI models mature, the emphasis is shifting toward optimizing inference-the process of applying trained models to generate real-time outcomes. This evolution is particularly advantageous for edge-based AI. 

For emerging companies, a focus on inference presents an opportunity to enter the AI market without investing heavily in model training. By specializing in inference solutions for edge devices, startups and smaller enterprises can compete effectively, offering tailored, efficient AI capabilities. The availability of open-source foundational models further empowers these players, enabling them to customize existing technologies for specific edge-based applications. This democratization of AI fosters innovation and allows for agile, industry-specific solutions. 

As 2025 approaches, the rise of tailored AI inference at the edge is set to revolutionize AI applications across many industries. The transition from a training-focused paradigm to an inference-centric approach is revealing new possibilities for real-time, privacy-conscious AI solutions. This shift empowers established companies and even startups, to deliver smarter, faster, and more secure offerings tailored to the many different types of use cases that exist in our technology-first world. By embracing this trend, businesses can unlock the potential of AI to deliver instant, personalized, and cost-effective solutions, meeting the demands of an increasingly data-driven world.

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ABOUT THE AUTHOR

Yoram Novick 

Yoram Novick is the President and CEO of Zadara. He has deep expertise in enterprise systems, cloud computing, storage and software and a proven track record of over 25 years of building successful startups. He is known as a company founder, CEO, and former board member and advisor to various technology companies such as Topio, Maxta, Storwize, Druva, and Kapow.

Yoram holds 25 patents in the systems, storage, and cloud domains. He holds both a bachelor's and a master's degree in computer science from Ben-Gurion University of the Negev.

Published Monday, December 09, 2024 7:33 AM by David Marshall
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