🤖 NVIDIA’s Scaling AI Inference To Power Next-Gen AI

🔋NVIDIA’s Scaling AI Inference To Power Next-Gen AI

☁️The Largest Cloud Computing Deal Ever Made

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A context window refers to the number of tokens an advanced AI model can process or “remember” when generating an output.

A token is a word or part of a word. For example, “yesterday” might be broken into three tokens: “yes,” “ter,” and “day.”

AI inference refers to how well an advanced AI model can apply the information it was trained on to generate useful outputs.

AI inference becomes more challenging as the context window expands. In other words, the more information you give an advanced AI model to work with, the harder it is for that advanced AI model to quickly, accurately, and consistently generate useful outputs.

So, why does this happen?! When advanced AI models have to process huge amounts of tokens, they hit a computational wall: a point where the GPUs running the advanced AI models simply can’t keep up.

“Rubin CPX” solves this issue by combining everything an advanced AI model needs into a single, super-efficient specialized computer chip that’s optimized for AI inference.

“Rubin CPX” enables advanced AI models to process significantly larger amounts of tokens without compromising on speed or accuracy. It delivers over 30 PetaFLOPs of compute; that’s 30 quadrillion mathematical calculations per second!

This breakthrough paves the way for enabling advanced AI models to handle more complex tasks while maintaining continuous context to deliver smarter, smoother, and stronger performance.

On Wednesday, Oracle’s stock rose 39.95% to $328.33 per share, signaling the software company’s largest single-day gain since 1992. To put this into perspective, Oracle CEO Safra Catz got $412 million richer in less than 6 hours.

Oracle gained $244 billion in “market cap” (i.e., the total value of a publicly traded company’s shares) within a single day, making Oracle worth more than $922 billion.

It’s all because Oracle announced it signed four multi-billion-dollar contracts to provide cloud computing services for AI developments. As a result, the software company expects AI-related cloud computing revenue to reach $144 billion by 2030.

Here’s the catch: OpenAI must raise tens of billions of dollars every year to afford Oracle’s cloud computing services. At the same time, Oracle needs to spend billions of dollars every year to build, maintain, and upgrade the AI infrastructure OpenAI depends on.

In other words, they’re both stuck in a billion-dollar spending spree, and if either side stumbles financially, the whole thing collapses.

Context: I want to better understand how my beliefs influence the way I interpret information,

Clarity: but I often struggle to identify how my biases guide, direct, or inform the assumptions I make.

Guidance: Can you help me analyze my {Insert Specific Opinion} regarding {Insert Specific Topic} based on {Insert Specific Worldviews}, and identify any potential hidden biases that may be shaping my assumptions?

Nuclearn closed a $10.5M Series A to automate day-to-day nuclear power plant operations.

Motion raised a $38M Series C to deploy AI-powered employees.

Cognition landed a $400M Series C for an AI-based software engineer.

Block (Remote): Software Engineer Intern, Summer 2026

NVIDIA (Austin, TX): AI Developer Tech. Engineer, Entry-Level

Microsoft AI (Mountain View, CA): Brand Designer, Mid-Level

Meta (London, UK): Data Engineering Manager, Analytics, Senior-Level