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:

💡 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:

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.

💡 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. ➡️

PERPLEXITY 🔥: Windows users with NVIDIA RTX GPUs can now use Portable Computer powered by local models! > Support for local MCPs and scheduled tasks has also been added. > Earlier, Portable Computer was introduced for NVIDIA DGX Spark devices. > Now, Portable Computer is also available on Windows PCs with a supported NVIDIA RTX GPU with 24GB of VRAM or higher. Models available locally 👀 - PPLX 27B (Perplexity post-trained Qwen 3.8 27B) - Qwen 3.8 27B (stock) is not available on Windows - Nemotron 3.5 Lightning (~30B MoE, ~3B active) is marked as "Coming Soon"

🚀 Sol-H3 on DGX Spark: 768p in Under a Minute 🤩 Monday: 8×B300, 5s 768p in 1.65s — faster than playback. Today: the same stack on one desktop Spark — about 56s hot E2E. Five seconds of 1344×768 video at 24 FPS with stereo audio, on a single NVIDIA DGX Spark (GB10). Two-stage, not the datacenter profile: 384p H3 draft → latent ×2 → H3-to-LTX VAE adapter → 768p LTX refine → VAE decode No decode/re-encode between stages. Stage 2 is conditioned on the draft latent, so Gemma stays off the box. Quantized weights stay resident; sparse attention cuts the rest. Stage 1 takes any MiniMax-H3 few-step LoRA. Timing is hot E2E (encode → both stages → video/audio VAE); cold start and MP4 mux are separate. Apache 2.0. Server was realtime. Edge is one box, under a minute. 🔗 Amazing team effort—full credits in the blog. @haopengl33 @lawrence_cjs @yitongli165665 @shanasaimoe ,Jingyu Xin, @HaochengXiUCB @songhan_mit

PERPLEXITY LAUNCHES FULLY LOCAL AI AGENTS WITH $NVDA Perplexity is launching Portable Computer, a local version of its agentic Computer platform built with NVIDIA that can run models, tools, files and multi-step AI workflows entirely on-device. The key difference: local work uses zero Perplexity credits and carries essentially zero marginal token cost. Tasks start locally by default, and the system asks permission before sending any step to a frontier cloud model. Hardware support starts with NVIDIA DGX Spark and Linux PCs with RTX GPUs carrying at least 24GB of VRAM, roughly an RTX 3090 or newer. Windows support is planned for September. At launch, users can run Qwen 3.8 27B or Perplexity’s post-trained PPLX 27B locally, with NVIDIA Nemotron 3.5 Lightning coming next. The platform bundles the full local stack: • Model inference • Agent harness • Tools and connectors • Security sandbox • Local file access • Gmail, Google Drive, GitHub and Slack integrations Perplexity says PPLX 27B scored 85.4% on its internal Local Knowledge Work Bench, versus 82.6% for Qwen 3.8 27B running through its own Computer harness. On BrowseComp, its local system scored 66.7%, while using 70% fewer tokens and 51% less time than the Pi harness. The hybrid setup is also notable. On Terminal Bench 2.1: • Fully local Qwen: 59.6% at near-zero marginal inference cost • Local + Claude Opus 5 advisor: 73.0% at ~$0.415/task • Claude Opus 5 alone: 82.4% at ~$0.65/task Before any cloud escalation, Perplexity says the system scans outgoing context for PII and shows users what data would leave the device. The remote model only returns text guidance and never directly accesses local files or tools. NVIDIA also says the hardware can scale beyond a single machine. Two DGX Sparks can run larger frontier-class open models, while four can handle models such as GLM 5.2 or Nemotron Ultra.