Nscale to Acquire Anyscale, Maker of the Open-Source Ray Framework
GPU cloud provider Nscale has agreed to buy Anyscale, the startup behind the open-source Ray framework, in a deal reported at roughly $1.65 billion.
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AI compute is the specialized processing power needed to train and run AI models, and its supply has become a central constraint on the industry. This hub covers the GPU and data-center capacity race, shortages, and the deals that secure compute.
GPU cloud provider Nscale has agreed to buy Anyscale, the startup behind the open-source Ray framework, in a deal reported at roughly $1.65 billion.
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A wafer-scale chip turns an entire silicon wafer into one giant processor instead of cutting it into hundreds of separate chips. Here's how it works, and why it's built for fast AI inference.
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Capital expenditure — capex — is what tech giants spend building AI data centers and chips. Here's what it means when that spending pushes a profitable company's free cash flow negative.
Read more →Alphabet posted its first quarterly free-cash-flow deficit as AI spending pushed capital expenditures to a record $44.9 billion, and raised its 2026 capex outlook to $205 billion.
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A single AI chat reply uses a fraction of a watt-hour, but billions of daily queries and the data centers behind them add up to a fast-growing power and water bill.
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Circular AI financing is the web of equity stakes, loans, and purchase deals linking chipmakers, cloud providers, and AI labs — and central banks now call it a systemic risk.
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A GPU is a chip built to do thousands of simple calculations at once. That's exactly what training and running AI models requires, which is why GPUs became the AI industry's most valuable hardware.
Read more →Monzo co-founder Tom Blomfield is taking leave from Y Combinator to join Anthropic's compute team, as the AI lab races to secure the computing power it needs to keep scaling Claude.
Read more →Meta plans to begin manufacturing its in-house "Iris" AI chip in September, aiming to double its data center computing capacity to 14 gigawatts by 2027, an internal memo reviewed by Reuters shows.
Read more →AI compute is the specialized processing power needed to train and run AI models. Demand has far outpaced supply — here is why the shortage persists and what it means for the industry.
Read more →Google told Meta in March it could not supply the computing capacity Meta had requested for its Gemini AI usage, disrupting internal projects and forcing Meta to push employees toward its own models.
Read more →The Google DeepMind spinoff signed a three-year, $6.3 billion computing contract with SpaceX's Colossus data center, giving it the GPU scale needed to challenge closed AI giants with open-weight models.
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