exactly. Anthropic's biggest constraint is compute, by far. OpenAI has – in this regard – a much better position
AI HARDWARE
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Conversation between Nvidia and HWChase17
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YUVA AI for ALL Course is making AI education more accessible and inclusive across India. By reducing language barriers, this course empowers more people to understand AI, engage with it, and use it in their everyday lives. Start learning today → https://
ndu.digital/productpage -
World’s Largest Chip Fabrication Facility Under Consideration
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This is one of several locations under consideration for what will be the largest and most advanced chip fabrication facility in the world
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LLM pricing per token mirrors SaaS era, buy a GPU
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Charging $ per 1M tokens
Is basically the SaaS era of LLMs That’s why I keep saying Buy a GPU -
OpenAI Accelerates AI Agent Phone Development Ahead of IPO
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Major leaks about the new OpenAI Phone: -OpenAI is reportedly accelerating development of its first AI agent phone, with mass production potentially starting in the first half of 2027. -One possible reason is strategic timing: the device could support a stronger IPO narrative
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SK Hynix and Samsung Lead in AI Supply Chain Value
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I'm European, it feels good to fight back and hold the line
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Texas confirms massive blue collar talent bottleneck for AI infrastructure
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No it literally says 458,900,000 page views and 327,800,000 visitors
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Choose Hardware First, Then the Inference Engine Follows
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You don’t pick an Inference Engine You pick a Hardware Strategy Then the Engine follows Inference Engines Breakdown (Cheat Sheet at the bottom) > llama.cpp
runs anywhere
CPU, GPU, Mac, weird edge boxes
best when VRAM is tight and RAM is plenty
hybrid offload, GGUF, -
Cerebras Inference Ecosystem Update: Open Models, Cloud, and Integrations
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Cerebras inference ecosystem, updated: open-weight model families, cloud providers, integrations. https://
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OpenAI Plans to Spend $50 Billion on Compute This Year
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Huge number reveal from OpenAI's Greg Brockman in court this am: OpenAI expects to spend $50 billion on compute this year. This gives a sense for how expensive it is *just to provide the computing power* for one of the world's most powerful AI companies.