As AI shifts from chatbots to autonomous agents, open models are serving market demands for full control over where AI runs and how it’s deployed and evolves.
If Nvidia proceeds with the takeover, the transaction will be a part of Nvidia’s increasingly aggressive investments across the AI ecosystem that spans from hardware to models to software. Last week, Nvidia agreed to pay…
As AI agents and trillion-parameter workloads become mainstream, the performance of AI infrastructure depends not only on compute, but on how compute, memory, storage, networking and software are designed together as a u…
For the second quarter of Nvidia’s FY2027, which ended on July 26, 2026, the company’s GAAP revenue hit a record $96.221 billion, up 18% quarter-over-quarter (QoQ) and 106% compared to the same quarter a year ago. Nvidia…
According to OpenRouter data , agentic AI workloads consume 15x more tokens than a simple chat request. Why?
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Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support. They may have strong customer demand and rap…
A Rubin GPU carries 288GB of HBM4 , roughly 576 times the memory of a single LP30, so a 31-billion-parameter model at FP8 needs on the order of 62 LPUs to hold its weights, and a large mixture-of-experts model runs into …
To explain how it works, let us compare OpenAI’s and Nvidia’s approaches. Nvidia’s standard system-level decomposition is primarily two phases: prefill and decode. Prefill is generally compute-bound (which is why Nvidia …
Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bottleneck isn’t just …
