As companies across the Asia Pacific region accelerate their AI initiatives, many are confronting increasing challenges related to infrastructure costs and performance. The need for faster, more scalable AI inference solutions is driving a significant shift toward edge computing, aiming to optimize real-time decision-making and reduce reliance on traditional centralized data centers. Infrastructure Challenges Hindering
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All posts tagged in AI Hardware
Google has revealed a bold plan to boost its artificial intelligence infrastructure by a staggering 1000 times over the next four to five years. This move shows how committed Google is to staying at the forefront of AI development and supporting the rapid growth of advanced AI applications around the world. The company aims to
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Artificial intelligence is pushing hardware makers, especially Apple, to pack more memory into their devices. This shift is making high-RAM computers more common, and that could mean more money for everyone buying new tech. As AI applications demand more power, companies are seeing costs go up across the board—from memory chips to energy bills. This
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Jiga, an innovative manufacturing platform powered by artificial intelligence, has secured $12 million in a Series A funding round led by Aleph. Based in Tel Aviv, Jiga helps companies streamline the complex process of sourcing custom hardware parts. Its platform is already trusted by major organizations like NASA and Siemens, highlighting its growing influence in
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Dell Technologies and NVIDIA have announced a new phase of their AI partnership. Their latest updates aim to make running different AI tasks simpler and faster. This comes at a good time as more companies look to expand their AI efforts but face challenges with hardware, software, and data management. Streamlined Deployment and Improved Performance
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Big changes are underway in the world of AI computing. Microsoft, NVIDIA, and Anthropic have teamed up to create a new alliance focused on pushing the limits of AI hardware and software. This partnership aims to build a more diverse and efficient ecosystem for training and deploying powerful AI models, moving away from reliance on
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Many companies are realizing they don’t need the newest and most powerful GPUs to get good results from AI. For years, the story was that top-tier GPUs were essential for training big AI models and running complex tasks. But that’s not entirely true for most everyday business applications. People often think that only the latest,
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Nvidia is making big moves to expand its influence beyond just chips. The company wants to control the entire stack from hardware to software, especially in AI and high-performance computing. While this growth brings impressive technology, some experts warn it could lead to a dangerous dependence on Nvidia’s ecosystem. Nvidia’s New Open-Source AI for Physics
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Wall Street is buzzing with optimism about next year’s technology investments. A big driver behind this confidence is the rapid global push to build AI infrastructure. Companies are borrowing huge sums to develop massive data centers and powerful compute systems, fueling a new wave of debt in the tech sector. The Rise of AI Infrastructure
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Duality Technologies has announced a major step forward in privacy-focused artificial intelligence. The company now supports Google Cloud’s Confidential Computing platform, including NVIDIA GPU-powered confidential virtual machines. This allows large AI tasks, like training and running large language models, to be done securely on NVIDIA GPUs with full data protection. It’s a big move for
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