Google Cloud and Nvidia Expand Confidential AI Infrastructure
Google Cloud is extending Confidential Bare Metal support to Nvidia GB300-based A4X Max instances, adding protection for AI models and data.
Compute is the total processing resource used to train or run an AI system. It may be described through chip hours, floating-point operations, accelerator counts, memory capacity, energy consumption, or cloud cost. Training a large model can require many processors working together for weeks, while inference compute is spent each time the deployed model handles a request. More compute can enable larger experiments and models, but results also depend on algorithms, data quality, hardware utilization, and software efficiency. Because compute affects cost, latency, energy use, and access to advanced AI, it is both a technical design constraint and an important economic factor.
Google Cloud is extending Confidential Bare Metal support to Nvidia GB300-based A4X Max instances, adding protection for AI models and data.
Anthropic is discussing another $45 billion Nscale agreement for West Virginia computing capacity, adding to an existing commitment as Claude demand grows.
DeepSeek has released DeepInfer, its production inference engine, with support for V4.1 Flash on Nvidia SM100 and Huawei Ascend 950 hardware. The public release covers offline inference.
Anthropic says Claude-driven development pushed code changes past 30,000 a day, turning Git infrastructure into a bottleneck and leading to its new Scion source-control system.
Tether has invested in AIsa, which combines AI-agent access to data, models and APIs with usage-based payments in fiat or stablecoins.
Anthropic will share Q3 results with investors as it weighs a potential $2 trillion IPO. Revenue definitions and compute costs will be central to the pitch.
Navitas and Nvidia have developed a 24kW board that converts 800V to 12V in one stage, targeting more efficient power delivery for AI data centers.
Microsoft has put Cisco Silicon One P200-powered systems into production, adding deep-buffer routing capacity for its growing cloud infrastructure.
Nvidia and Cisco outline USFP optics for denser, more efficient AI networks, alongside new Cisco systems for scaling and managing data centers.
Microsoft plans to make Maia its primary AI inference system, with Maia 300 arriving in 2027 and a larger role for its homegrown chips.