DEEPX, a leader in ultra-low-power on-device AI semiconductors, unveiled a next-generation AI video analytics platform on October 8, aimed at surpassing traditional GPU-centric designs. This innovative solution combines DEEPX accelerators and Ampere CPUs, reflecting a fundamentally new compute architecture that promises higher density, reduced power consumption, and greater cost efficiency for large-scale Vision AI deployments.
The newly launched system integrates DEEPX’s DX-H1 Quattro AI accelerator with Ampere’s high-efficiency CPUs, creating an optimized pipeline tailored to modern video management system (VMS) requirements. As the global video management market transitions from storage-centric operations to real-time AI analytics, the industry is grappling with the inherent limitations of GPU architectures, including high heat generation, significant power consumption, and limited scalability. DEEPX’s platform fundamentally addresses these challenges, establishing a marked shift away from conventional GPU-only solutions.
AmpereOne® CPUs are designed to rapidly decode hundreds of high-resolution CCTV streams, while the DX-H1 Quattro is dedicated entirely to high-density AI inference. This division of responsibilities enables remarkable throughput, allowing a single server to analyze over 200 cameras simultaneously and sustain more than 8,000 real-time AI inferences each second. With scalable performance exceeding 400 Tera Operations Per Second (TOPS), the platform is well-suited to support mission-critical infrastructures such as smart cities, advanced video surveillance systems, and industrial control environments.
The system’s power efficiency is equally groundbreaking; while traditional CPU + GPU servers often exceed 1,200 watts to achieve similar workloads, the DEEPX platform operates between 400 and 750 watts, depending on configuration. This substantial reduction in power requirements translates into lower operational costs for 24/7 monitoring centers, which is increasingly crucial as cities and enterprises expand their AI deployments globally. The AmpereOne® architecture excels in decoding performance while minimizing heat output, allowing a single server to accomplish tasks previously requiring multiple racks of GPU hardware.
Engineered to support a wide array of Vision AI workloads—including YOLO-based object detection, behavior analysis, CLIP-based semantic search, and real-time RTSP encoding—the DEEPX solution provides unified analytics from edge deployments to hyperscale data centers. Compatibility with major server original equipment manufacturers (OEMs) such as ASRock Rack, NEXT Computing, Gigabyte, System76, and Supermicro, along with seamless integration with leading VMS platforms like Network Optix and ZoneMinder, enables immediate deployment for customers.
Lokwon Kim, CEO of DEEPX, emphasized the significance of this technological advancement, stating, “The combination of the DX-H1 Quattro and AmpereOne® is redefining what’s possible in the global VMS industry. This is the platform that markets facing extreme cost and power pressures have been waiting for.” He further noted DEEPX’s intent to aggressively target multi-billion-dollar markets, including smart cities, video surveillance, transportation, and industrial automation.
Jeff Wittich, Chief Product Officer at Ampere, highlighted the architecture’s capability to manage intensive analytics workloads and continuous data ingestion without sacrificing power efficiency. “That combination is crucial for customers deploying secure, reliable video infrastructure across everything from major enterprise deployments to edge-based systems,” he said.
Looking ahead, DEEPX plans to expedite initial deployments across video surveillance, large-scale smart city initiatives, infrastructure monitoring for manufacturing and logistics, and real-time traffic and security control. The company intends to introduce the platform to global enterprise customers starting in the first half of 2026 through a series of international exhibitions, technical webinars, and roadshows, signaling a significant shift in the video analytics landscape.
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