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 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.