A decade after #NVIDIADGX-1#, #NVIDIAVeraRubin# redefines what an AI factory looks like. Built through extreme co-design, Vera Rubin delivers a POD-scale platform purpose-built for global-scale AI infrastructure. From model training to agentic AI workloads, it’s the foundation for what comes next. 📍 Stop by our booth on the #ISC26# show floor to see our demos 🔗 Learn more:

Huge congratulations to the @SpaceX team on a historic IPO debut. Fueling the next frontier of space and AI. 🌌 NVIDIA's partnership with SpaceX spans nearly a decade, from hand-delivering the world's first #NVIDIADGX-1# supercomputer in 2016 to the custom DGX Spark handoff at Starbase. Together, we've been pushing the boundaries of accelerated computing to help power the future of space exploration.

💡 What breakthroughs have APAC enterprises achieved with #NVIDIADGX#? Across the region, leaders like @HonHai_Foxconn, @MediaTek, @official_naver, and @NTTData are utilizing DGX SuperPOD to build agentic AI factories, scale intelligence from edge to cloud, and deliver sovereign AI. #DecadeOfDGX# 🔗 Learn more about how customers use the DGX platform as the blueprint for the modern AI factory:

💡 One AI factory. 1,016 NVIDIA Blackwell Ultra GPUs. Over 9,000 petaFLOPs of AI performance. @EliLillyandCo and NVIDIA launch LillyPod, the world's first #NVIDIADGX# SuperPOD with DGX B300 systems to accelerate drug discovery, medical research, operational efficiency, and enhance industry collaboration. Together, by combining science, data, and compute power, we're breaking new ground for AI in life sciences. Learn how we're advancing the broader biotech ecosystem. ➡️

People made fun of @AlexFinn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an @nvidia DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) @Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A @NousResearch Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.