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NVIDIA BlueField-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories

24 August 2026 at 15:00
BlueField-4 render.Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users,...BlueField-4 render.

Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users, agents, applications, data sources, and storage systems to massively accelerated compute at multi-terabit bandwidth per server, making dedicated DPU processing essential for line-rate networking, storage, and security.

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Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI

23 July 2026 at 18:15
What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale....

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale. These factories are now tasked with powering agentic workflows that reason, plan, use tools, verify intermediate results, and execute complex multistep tasks across vast contexts. Agentic workloads are not defined by a single prompt…

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Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField

16 July 2026 at 16:00
Decorative image.Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage...Decorative image.

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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Maximize AI Factory Energy Efficiency Through Full-Stack Inference and Training Optimizations

23 June 2026 at 16:30
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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Designing Production-Ready Battery Energy Storage Systems for AI Factories

10 June 2026 at 15:00
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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NVIDIA DSX OS Delivers Open, Modular Software for Operating AI Factories at Scale

1 June 2026 at 03:36
AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale...

AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale faster, operate more efficiently, and lower the cost of intelligence across the five-layer stack: energy, chips, infrastructure, models, and applications. NVIDIA DSX platform provides the complete playbook for designing, simulating, building…

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