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Pushed By GenAI And Front End Upgrades, Ethernet Switching Hits New Highs

But virtue of its scale out capability, which is key for driving the size of absolutely enormous AI clusters, and to its universality, Ethernet switch sales are booming, and if the recent history is any guide, we can expect Ethernet revenues will climb exponentially higher in the coming quarters as well. …

Pushed By GenAI And Front End Upgrades, Ethernet Switching Hits New Highs was written by Timothy Prickett Morgan at The Next Platform.

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Inside the NVIDIA Rubin Platform: Six New Chips, One AI Supercomputer

AI has entered an industrial phase. What began as systems performing discrete AI model training and human-facing inference has evolved into always-on AI...

AI has entered an industrial phase. What began as systems performing discrete AI model training and human-facing inference has evolved into always-on AI factories that continuously convert power, silicon, and data into intelligence at scale. These factories now underpin applications that generate business plans, analyze markets, conduct deep research, and reason across vast bodies of…

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NVIDIA Blackwell Enables 3x Faster Training and Nearly 2x Training Performance Per Dollar than Previous-Gen Architecture

AI innovation continues to be driven by three scaling laws: pre-training, post-training, and test-time scaling. Training is foundational to building smarter...

AI innovation continues to be driven by three scaling laws: pre-training, post-training, and test-time scaling. Training is foundational to building smarter models, and post-trainingβ€”which can include fine-tuning, reinforcement learning, and other techniquesβ€”helps to further increase accuracy for specific tasks, as well as provide models with new capabilities like the ability to reason.

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Streamline AI Infrastructure with NVIDIA Run:ai on Microsoft Azure

Modern AI workloads, ranging from large-scale training to real-time inference, demand dynamic access to powerful GPUs. However, Kubernetes environments have...

Modern AI workloads, ranging from large-scale training to real-time inference, demand dynamic access to powerful GPUs. However, Kubernetes environments have limited native support for GPU management, which leads to challenges such as inefficient GPU utilization, lack of workload prioritization and preemption, limited visibility into GPU consumption, and difficulty enforcing governance and quota…

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