Companies today are being asked to do more with data than ever before. Bigger AI models, faster insights, and workloads that don’t stay in one place, it’s a lot to keep up with. Traditional infrastructure just isn’t built for this kind of speed and flexibility.
The answer isn’t about throwing more hardware at the problem. It’s about building smarter, more agile infrastructure that adapts as demands change. And that’s where scale-out and increasingly, a blend of scale-out and scale-up come into play.
The rugged edge computing landscape is becoming increasingly complex with new generations of technologies, such as the latest AI focused GPUs, releasing annually rather than every 2-3 years. Whether the end application is commercial or defense, rugged edge servers must not only deliver cutting-edge compute performance but also withstand extreme environmental conditions.
When the PCI-SIG formally added support for 675W add-in card devices in the PCI Express Card Electromechanical (CEM) specification in August 2023, NVIDIA’s most powerful CEM GPU, the NVIDIA H100 80GB had a maximum power consumption of 350W. While some devices were starting to push the limits of datacenter thermodynamics – high density systems of many 675W devices seemed like a distant reality. However, with power constraints uncapped and the need for higher performing GPUs skyrocketing, the industry quickly came out with devices taking full advantage of the new specification capability. NVIDIA quickly replaced the H100 80GB with the H100 NVL, increasing power density to 400W. While this small jump was manageable for existing installations, NVIDIA then dove all-in with the H200 NVL released in late 2024 at 600W. The rapid transition from 350W to 600W has put power and cooling technologies in the spotlight in a race to solve this next generation challenge.
The advent of technology has always brought about significant changes to various industries, and the transportation sector is no exception. Among the most transformative innovations in recent years is the development of autonomous vehicles, particularly trucks. The potential for autonomous trucks to revolutionize freight transport is immense, raising the fundamental question: will these technological advancements make human drivers obsolete? To explore this question, we must consider the current state of autonomous driving technology, the economic implications, and the societal impact of removing human drivers from the equation.
The integration of artificial intelligence (AI) into military operations has revolutionized battlefield strategies, decision-making, and operational efficiency. Among these advancements, AI inference nodes deployed directly on soldiers represents a cutting-edge innovation. These nodes, compact computational devices, enable real-time AI processing and analytics, empowering soldiers with enhanced situational awareness, decision support, and operational effectiveness. However, such technology also brings challenges, particularly in power management, size, and weight constraints. This blog delves into the advantages and disadvantages of implementing AI inference nodes on soldiers, focusing on these critical aspects.
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