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WHTop → 📋 News → Nebius Raises $775M Secured Debt to Finance AI Cloud Growth
Nebius Raises $775M Secured Debt to Finance AI Cloud Growth
📅 - Nebius has secured approximately $775 million in its first senior secured debt facility, using deployed GPU infrastructure and contracted customer cash flows as collateral to finance further AI cloud expansion. The deal gives the company a template for funding capital-intensive capacity without relying entirely on corporate equity, as hyperscale commitments increasingly reshape infrastructure financing markets worldwide.
The financing matures on October 31, 2030 and carries an interest rate of SOFR plus 2.50 percent. More important than the headline amount is what sits underneath it. The debt is supported by operating GPU infrastructure and revenue contracted with an investment-grade [...][... Check source for end of article ...]
📅 - Duos Edge AI Wins $111M Colocation Contract With Hyperscaler - Duos Edge AI has signed a five-year colocation agreement worth more than $111 million to supply 10 MW of critical IT load at its Columbus, Georgia campus. The deal, with an unnamed investment-grade hyperscaler, would double planned campus capacity to 20 MW by late 2026, concentrating substantial contracted demand on a facility still moving into revenue production.
For a relatively small infrastructure developer, the numbers are consequential. Duos Edge AI says an initial 10 MW deployment at Columbus should start generating revenue in August, while the additional 10 MW covered by the new agreement is expected to become available during the fourth quarter. If both schedules hold, the campus [...]
📅 - IBM CEO Says Internal Execution Failures Drove Q2 Shortfall - IBM CEO Arvind Krishna has blamed the company's second-quarter shortfall on execution failures inside IBM, saying teams reacted too slowly as customers redirected spending and delayed transactions. The admission is unusually direct for a technology incumbent and shifts attention from macroeconomic excuses toward sales discipline, account management and IBM's ability to respond when enterprise buying patterns change.
Krishna did cite external disruption. Customers moved capital toward servers, storage and memory during the final weeks of June, apparently trying to secure constrained equipment before anticipated price increases. Cybersecurity concerns also consumed management attention [...]
📅 - Databricks Lines Up New Funding at a $188 Billion Valuation - Databricks has signed a term sheet for fresh strategic financing that would value the data infrastructure company at $188 billion, with Coatue leading the round. The deal, expected to close later this summer, gives Databricks more capital for AI products, acquisitions and research - while raising fresh questions about valuation discipline and enterprise demand at that price.
The company is not disclosing the size of the round, which limits any serious assessment of dilution, runway or how much strategic influence participating investors may gain. What is visible is the destination of the money. Databricks plans to expand Unity AI Gateway, its control layer for model access and spending, [...]
📅 - Tech Sovereignty Gap Leaves the US and China Firmly in Front - Forrester expects global technology sovereignty to barely improve through 2030, despite governments pouring money into semiconductors, AI and domestic infrastructure. Its new 14-country index projects the average score rising from 39% in 2025 to just 40%, leaving the United States and China far ahead while Europe and other economies remain dependent on foreign technology suppliers and platforms.
The forecast puts numbers against a problem governments have spent several years trying to solve with subsidies, procurement rules and increasingly explicit industrial policy. Technology independence is expensive. Worse, the underlying supply chains are interconnected enough that replacing one [...]
📅 - Stop Wasting GPU Cycles: Multi-Model Serving with SGLang - As artificial intelligence applications scale, the cost and efficiency of deploying Large Language Models (LLMs) become paramount. A common bottleneck for AI developers and enterprise IT teams is highly inefficient GPU utilization. VRAM fragmentation and sluggish inference times can severely limit your hardware’s potential, often forcing you to purchase additional, expensive GPU resources unnecessarily.
iDatam’s latest technical tutorial tackles this exact issue, introducing a highly effective solution: deploying SGLang on a bare-metal GPU server.
Why SGLang is a Game-Changer for AI Inference SGLang is designed specifically to optimize how multiple models interact with your server's VRAM.
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