The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute...

The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute infrastructure faster than ever. AI factories—data center-scale systems that continuously convert data and energy into intelligence—are being deployed to meet this insatiable demand. This AI factory approach to the data center has…

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Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage...

Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage accesses, and network transfers before a final answer is produced. As more agents run at once and carry context across steps, users, tools, services, and sessions, infrastructure must move, protect, retrieve, and reuse data fast enough to keep…

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Agentic systems turn model reasoning into action through multi-step workflows that combine inference, tool use, code execution, retrieval, orchestration, and...

Agentic systems turn model reasoning into action through multi-step workflows that combine inference, tool use, code execution, retrieval, orchestration, and result handling. As these systems scale across the AI factory, performance depends not only on GPU acceleration, but also on the CPU work that happens between model steps. Across the creation and deployment of an agentic system…

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AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on...

AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on behalf of a user. This unlocks productivity, but can also give agents access to sensitive enterprise data and the ability to complete tasks and take action across business systems, making a secure, governed environment essential.

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Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating...

Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating tokens for customers. And most sites are capped at a fixed power level provided by a regional provider. Under these conditions, performance per watt becomes a key efficiency metric that directly translates to token costs.

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AI factories are changing what data-center infrastructure must do. Unlike traditional data centers, AI factories are built to manufacture intelligence at scale....

AI factories are changing what data-center infrastructure must do. Unlike traditional data centers, AI factories are built to manufacture intelligence at scale. They run power-dense training and inference workloads, increasingly support agentic and reasoning models, and must deliver predictable performance even as compute demand shifts rapidly. In this environment…

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The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented...

The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing, AI factories enable enterprises to train, fine-tune, and deploy AI with greater speed and efficiency. This new class of infrastructure also introduces a fundamentally new attack surface spanning…

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Each wave of AI has created a new scaling law. Pretraining scaled intelligence through larger datasets, more parameters, and massively parallel GPU systems....

Each wave of AI has created a new scaling law. Pretraining scaled intelligence through larger datasets, more parameters, and massively parallel GPU systems. Post-training scaled usefulness through instruction tuning, and re-balancing GPUs for generative inference. Test-time scaling improved reasoning by giving models more generated tokens for thinking. Now, agentic AI and reinforcement…

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