A new beginning for Windows PCs starts tomorrow with the launch of NVIDIA RTX Spark , one PC built for creators, gamers and AI agents. All are invited to celebrate with NVIDIA and partners at launch events around the world. Share Your Spark Every PC has a story. What was your first Windows PC, and […]

NVIDIA today released Nemotron 3.5 Super VL, an open 120-billion-parameter model that delivers the fastest task completion and highest accuracy for specialized always-on agents. Joining it are two new edge models: Nemotron 3.5 Bolt 74B-A9B 24GB, optimized to balance intelligence and speed per GB for local agentic workloads, and Nemotron 3.5 Bolt E4B 4GB, optimized […]

Across industries, the U.K. is putting the full stack to work. The scale of deployment is growing fast, from enterprises transforming their businesses to startups solving some of the planet’s hardest problems. NVIDIA is putting that principle into practice, including through a five-year, $250 million compute commitment to strengthen the U.K.’s agentic safety and security […]

Keeping a city running means making decisions that affect residents every day , from when to repair a road to how to use energy more efficiently. Across the EMEA region, cities and local software, infrastructure and integration partners are using NVIDIA accelerated computing to build physical AI agents and digital twins that help teams make […]

The 14 October preprint proposes choosing guidance timesteps under a fixed budget and reports scoped text, biological-sequence and image experiments. Reviewed abstract, not reproduced results; a candidate cost/capability mechanism, not demonstrated customer economics or a separate frontier from compute optimisation.

Digital Jersey's dated statement proposes assessing demand, outcomes, governance, security, skills and commercial conditions with NVIDIA expertise. Later proof of value, public-sector involvement, funding or infrastructure procurement would need further consideration and approval. This is a Jersey exploration, not a Guernsey deployment, paid contract or proven citizen benefit. It qualifies the stronger deployment language in the supplier's wider city narrative.

October report linked by the14 October announcement. Text and figure pages checked. The output-token chart uses Enterprise/Business accounts and a28-day trailing window; SMB weekly-user growth uses a<=200 associated-user definition. The separate work-activity sample uses consumer plans. These denominators must not be pooled or read as all-EU adoption, task success, paid retention, revenue or worker benefit. Supplier-authored; OpenAI tools are also used for this analysis.

Companies in France are deploying agentic computing across industries spanning space, financial services, the public sector and scientific research, demonstrating how the country’s ecosystem has moved from ambition to application. France’s progress sits on deliberate groundwork. The AI Action Summit in February 2025 and Choose France Summits in 2025 and 2026 anchored major compute commitments [&#8230;]

NVIDIA describes a compute donation valued at USD250million over five years, delivered through UK cloud partners for agentic safety/security research. The valuation is the donor's announcement, not a disclosed cash transfer, third-party paid demand, booked supplier revenue or completed allocation. The sponsor has a direct commercial interest in its ecosystem; this is not independent validation of its investments.

Turning a simulation idea into a working application means assembling assets, connecting physics and rendering, and checking that the scene behaves as intended. Developers are combining frontier AI models with NVIDIA Omniverse libraries to help carry out that work , building applications for exploring scenarios, investigating failures and improving designs. Developers direct AI agents through [&#8230;]

The original engineering post reports a bounded browser task across model/cache/history conditions, with small per-condition samples. The headline compares a changed model and workflow against a capped baseline; it is not a universal model-price reduction or Asana margin result. Read the public methods/results post, not the linked full study or raw traces. The9 October OpenAI account reports the same study, not independent replication.

At a Microsoft event in San Francisco on Wednesday, NVIDIA founder and CEO Jensen Huang and Microsoft CEO Satya Nadella outlined how NVIDIA and Microsoft are co-engineering hardware and software for AI agents to run on Windows PCs. NVIDIA was founded because of Windows, Huang said. Now AI agents are coming to Windows. “If you [&#8230;]

Local AI is becoming more useful by the token. As AI agents move from experiments into everyday development, increasingly capable open models are shrinking to fit on more devices, giving builders more to run locally. Coming this month, NVIDIA DGX Spark will be available with 64GB of unified memory from top manufacturer partners , Acer, [&#8230;]

MIT describes adding chemical constraints before generative crystal design to improve computational stability and targeted properties. It reports computational screening gains for ordered crystalline structures, not universal materials coverage, experimentally qualified production, accepted cooling products or realised customer savings. Potential capture could move towards validated design tools and qualified materials. University account of its own collaboration; full journal paper not examined.

Microsoft forecasts approximately $175bn capex after lease reclassification; it says underlying investment expectations are unchanged. Limitation: Company guidance; includes AI and non-AI assets. Calendar-year guidance cannot be compared directly with a fiscal quarter or interpreted as energised compute. Existing research checked 2026-10-04. Microsoft reports $35.8bn cash paid for PP&E in FY26 Q4, separately from its $41bn capex measure. Limitation: Accounting expenditure is not commissioned capacity. Do not add the two overlapping measures or infer an AI-only allocation. Existing research checked 2026-10-04. Microsoft distinguishes lease classification from underlying infrastructure investment expectations. Limitation: Not physical capacity,cash cuts,AI-only allocation or general accounting advice. Fiscalquarter/calendarforecast and cashPPE/capex measures cannot be summed;current original transcript not a frozen point-in-time archive. Existing research checked 2026-10-12.

Google Cloud listed Gemini 2.5 Flash Flex/Batch input at USD 0.15 per million tokens and output at USD 1.25 per million tokens. Limitation: A token tariff is not cost per completed reliable task, latency, uptime, factual quality, human review or a full application bill. Prompt length, output length, grounding, caching, modality, region, commitment and model choice change cost. Google is the service vendor and prices can change. Existing research checked 2026-10-05.

arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an otherwise correct action may arrive too late. We introduce RealtimeBench, with 180 tasks across six game types, to evaluate agents while games continue running during inference. Paired evaluations show that GPT-6.1-sol outperforms the faster Jev when games are paused, but falls behind when they keep running. We propose Two-Timescale Offline Recursive Self-Improvement (TORSI): a slow teacher studi

arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examples until the task is covered. The open world asks for versatility --- taking up tasks a machine was not built for, from few examples and with little prior from a human designer --- and offers data that is grouped, not shuffled. Its tasks are not known in advance, so a machine must do inference-time learning (1st requirement). Modeling tasks as sharing latent factors and decomposing the training o

arXiv:2610.20822v1 Announce Type: new Abstract: Text-to-video models often generate the visual concepts requested in natural language prompts but fail to satisfy their intended ordering, timing, and persistence requirements. We propose representing these requirements as specifications in a formal Temporal Logic, and introduce Temporal Specification Guidance (TSG), a training-free inference-time framework that turns temporal specifications into executable objectives for guiding video generation during sampling. TSG scores traces of visual concept evidence using differentiable quantitative seman

arXiv:2610.20836v1 Announce Type: new Abstract: Evaluator-guided reasoning presents a local control problem at every step: trust the current answer, return a fallback, or spend more inference. We formulate this choice as a compute-aware three-action controller, scoring terminal actions by predicted answer success and expansion by a rollout-based continuation-success surrogate, with compute cost priced explicitly. A local action-gap analysis characterizes when score errors can change a decision, while an expected regret bound accounts for errors along the realized expansion path. Guided by this