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1

Productive, Durable, Fungible: How NVIDIA AI Factories Maximize Return on Investment (opens on the source site)

AI factories are built by the megawatt, even by the gigawatt. Each megawatt factory costs roughly $60 million, and AI factory operators will only commit capital on that scale with a clear view of the return on investment. Three key things shape AI factory returns: Earning capacity: What the factory could earn in a year […]

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3

NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut (opens on the source site)

System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments. […]

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4

NVIDIA NVLink Fusion Expands With NVHBM Custom High-Bandwidth Memory (opens on the source site)

The next wave of AI is placing new demands on infrastructure. As AI agents and trillion-parameter workloads become mainstream, the performance of AI infrastructure depends not only on compute, but on how compute, memory, storage, networking and software are designed together as a unified system. To help hyperscalers and AI innovators build the next generation […]

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5

How XPUs Meet a World-Class AI Factory (opens on the source site)

To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators. Hyperscalers and AI-native companies building custom XPUs must consider […]

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6

Up to 30x More Work Per Watt: NVIDIA Vera Rubin NVL72 Sets a New Efficiency Standard for AI Agents (opens on the source site)

According to OpenRouter data, agentic AI workloads consume 15x more tokens than a simple chat request. Why? Consider what happens when an AI agent researches a company for an investment decision. The agent queries financial databases, searches news and filings, invokes a sub-agent to run peer comparisons and model valuations, then synthesizes everything into a […]

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7

From Custom to Open: Scalable Network Probing and HTTP/3 Readiness with Prometheus (opens on the source site)

The Problem: Legacy Tooling and Its Limitations Currently, Slack utilizes a hybrid approach to network measurement, incorporating both internal (such as traffic between AWS Availability Zones) and external (monitoring traffic from the public internet into Slack’s infrastructure) solutions. These tools comprise a combination of commercial SaaS offerings and custom-built network testing solutions developed by our…

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8

Monitor Network Traffic with EtherApe (opens on the source site)

I wanted to count packets coming back and forth while telnet'ing to test out some different email protocols on our mail server and found this nifty little utility: it's called EtherApe. Supposedly it's a clone of Etherman, which, if it has clones, probably costs money. (http://picasaweb.google.com/lh/photo/jNWdHI8V5W1IIMh1W1NKYw?feat=embedwebsite">sudo apt-get install etherape There will be a few libraries that are dependencies, but I forget which they are.

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