We announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time. This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies […]
Month: August 2026
NVIDIA founder and CEO Jensen Huang is ranked No. 1 on Glassdoor’s Best CEOs list for 2026. In the just-released ranking, recognition is earned directly from the people who know their leadership the best — employees. Huang topped the list, with 99% of employees approving of the job he does. “As AI and shifting expectations […]
GeForce NOW is giving cloud gaming an extra-credit upgrade just in time for back-to-school season. The native Linux app for GeForce NOW is officially out of beta. GeForce NOW is also delivering new cloud optimizations that make Frame Generation feel even more responsive while streaming. On top of that, Performance members will see higher frame […]
Indonesia is taking charge of its AI future. This week, the Ministry of Communication and Digital Affairs (Komdigi), Indosat Ooredoo Hutchison (Indosat or IOH), NVIDIA and Universitas Gadjah Mada (UGM) launched the UGM Indosat NVIDIA AI Technology Center (NVAITC) in Yogyakarta — the country’s first university-based AI technology center. Established under Indonesia’s AI Center of […]
NVIDIA announced that it has secured land, power and shell (LPS) capacity through a partnership with SB Energy at the PORTS-Pike Technology Campus in Pike County, Ohio, to host NVIDIA compute…
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Securing the Infrastructure of Intelligence
AI factories are the defining infrastructure of the AI era—where compute transforms energy and data into intelligence that powers every business, industry and country. In the AI economy, compute is revenue. AI factories require a full stack of critical resources: advanced chips, packaging, memory, and networking – as well as land, power and shell. Just […]
A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene…
A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene often fails when object shapes, positions, or lighting change. Generalizing to these new conditions requires the policy to understand the tasks underlying physics, not just mimic the demonstrations. This ability comes from the backbone it’s…
Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI…
Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI agentic work. Optimized to run across a range of NVIDIA edge, desktop, and workstation AI platforms, Muse Glimmer delivers 20K tokens/sec on a single GPU, enabling always-on agents to process data locally and execute complex…
Building an AI agent does not end with choosing a single model. Each model has its own strengths, weaknesses, and cost profile, which can shift from one…
Building an AI agent does not end with choosing a single model. Each model has its own strengths, weaknesses, and cost profile, which can shift from one workload to another—or even within the same workload. For example, an agentic task may need classification for one step, reasoning for the next, and a smaller model for routine follow-up tasks. Sending every request to the largest model can…
Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning…
Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning model for every execution step adds cost and latency. NVIDIA Nemotron 3.5 Lightning is an open 30B mixture-of-experts (MoE) model with 3B active parameters built for that execution layer of always-on agents. It is designed for harnesses…