NVIDIA Asia Pacific

Robot policies are improving faster than our ability to evaluate them. 🤖 Our latest blog breaks down the benchmarking gap in generalist robot policies and what it takes to assess real-world readiness. 📖 Read the blog: nvda.ws/44YUxjM

One DGX Spark on your desk is powerful. Four is a local AI factory. NVIDIA DGX Spark now scales up to four clustered nodes, enabling inference on models up to 700B parameters — with near-linear performance scaling for fine-tuning and reinforcement learning workloads. Memory scales from 128GB on a single node all the way up for the full four-node configuration, supporting large context windows, high concurrency, and multi-agent workflows. For APAC enterprise teams building autonomous AI: each topology is purpose-built for your workload — from rapid local iteration to production-scale inference. And when you're ready to move to the cloud, Tile IR and cuTile Python carry your kernels across with minimal code changes. No rack. No rearchitecting. Just scale. 🔗nvda.ws/4w0RBPy

💡 Continuous software innovation is the force multiplier behind AI infrastructure — compounding inference performance, lowering cost per token, and increasing long-term value with every optimization. Open source accelerates this advantage. Leading AI frameworks like @PyTorch and inference engines such as @sgl_project and @vllm_project are built natively on NVIDIA CUDA, enabling research breakthroughs and software optimizations to unlock great performance on NVIDIA GPUs from day zero. Learn more: nvda.ws/4vw9LI7

Every company has specialized intelligence that sets it apart. With @langchain, we’re helping enterprises turn that knowledge into specialized agents they can customize, secure and improve over time. Learn more: nvda.ws/3SKngq1

AI is shifting from model training to always-on token production, and that shift demands a new business model. NVIDIA is partnering with AI clouds to deploy large‑scale, multi‑tenant AI factories through revenue-sharing and credit-support. This opens up compute access to the fast‑growing AI ecosystem of startups, model builders, enterprises, research organizations and regional AI players. 🔗 Read more: nvda.ws/3R8EooD

AI agents can now run for hours across your enterprise systems, which makes governing them a non-negotiable. 🔒 The NVIDIA Secure Agent Workspace Reference Design provides enterprises with a blueprint for securely deploying always-on agents. This is achieved by moving execution off the user's device into a managed workspace with controlled identity, network access, and policy enforcement. 📖 Learn how to govern autonomous agents in your enterprise AI factory: nvda.ws/4piTgh1

Why are open technologies for AI so important? How and why is NVIDIA building Nemotron? What can we learn from China’s AI efforts? Great conversation with Matt Turck. youtu.be/Oojrfdl42LI

🌍 20 million lives impacted every single year. That is the power of technology when it is purposefully engineered to serve humanity. In many low and middle-income countries, healthcare professionals face immense barriers such as unreliable internet, a lack of localized content, and language gaps that can delay critical care. To bridge this gap, Techies Without Borders built an AI-powered experimental platform that acts as a localized "doctor's assistant." Frontline workers can now type in a medical query and receive an instant, evidence-based answer in their own language. Thanks to NVIDIA AI and accelerated computing, medical insights that used to take 5 agonizing minutes to load are now delivered in just 2 seconds. And because it runs locally on edge devices, a lack of internet connection is no longer a barrier to saving lives. This isn't just a technical achievement; it's a lifeline for clinics facing severe physician shortages.

What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄 nvda.ws/3TlTCaw

NVIDIA Vera: The Max Single-Threaded CPU at Scale. Agentic AI systems run sequentially, each reasoning step, tool call, and code execution happens one at a time on the CPU. When that CPU slows under load, the entire agentic loop slows and valuable GPUs go underutilized. A max single-threaded CPU at scale keeps every agent step fast, even when the system is fully loaded. That's what NVIDIA Vera is built to do, and why Perplexity is already building on it. Read More: nvda.ws/4yanqai

GTC Taipei marked a milestone moment for AI and it’s not too late to join in. 🚀 Catch up at your own pace with NVIDIA CEO Jensen Huang's keynote replay, on-demand sessions, plus the latest updates from Taipei. 👉 Access on-demand content: nvda.ws/4aL3h0t #NVIDIAGTC

19 million developers can now do more with frontier physical AI tools. 🤗 We're expanding open robotics with @HuggingFace, bringing GR00T 1.7, an open VLA model for humanoid robots and Isaac Teleop directly into LeRobot. Read the blog ➡️ nvda.ws/4eQubq5 #MACHINA2026

When you train a video model for motion, only some clips actually matter. New from NVIDIA Research and recipient of an Outstanding Paper Honorable Mention at #ICML2026: MOTIVE shows exactly which training clips improve motion. MOTIVE re-weights training signals toward moving regions and away from static backgrounds, then scores each clip by its influence on motion. You can then curate a small high-influence subset to fine-tune on and get better temporal dynamics. Because it attributes motion rather than appearance, MOTIVE curates data that boosts VBench dynamics and wins 74.1% human preference against the base model.

Autonomous agents are ready. Enterprise governance is the missing piece. 🔒 AI agents working for hours on your behalf, connected to your live systems, running while your laptop is closed — this is where enterprise productivity is heading. Our Secure Agent Workspace Reference Architecture gives platform and security teams the identity controls, runtime policies, and audit infrastructure to get there at AI factory scale. Learn more 👉 nvda.ws/4wx5IvN

Scaling complex workloads for autonomous AI agents requires a robust data infrastructure. NVIDIA DGX Spark optimizes data processing pipelines, enabling enterprise-scale deployment of Agentic AI workflows with maximum compute efficiency. Stop compromising on token throughput. Build and scale your autonomous systems locally. 🔗 Find out more about DGX Spark: nvda.ws/4eOCTFq 🔗 Read more: nvda.ws/4f1hS9o

Data center-grade AI. Now on your desk. NVIDIA DGX Spark and DGX Station brings AI-factory-class compute directly into the enterprise—no rack, no cloud dependency. ✅ DGX Station packs 748GB of coherent memory and up to 20 petaflops of AI performance into a deskside system, supporting models up to 1 trillion parameters. ✅ DGX Spark clusters up to four systems into a compact desktop data center with near-linear scaling. For APAC enterprises in regulated industries, air-gapped configurations keep your data sovereign and your workflows compliant. And when you're ready to scale, the same GB300 architecture moves seamlessly to the data center or cloud — no rearchitecting required. nvda.ws/4gjRARY

The Nemotron family just passed 100M downloads! Huge thank you to the community building with us and showing what’s possible with open models. Cheers to OSS 🍾

NVIDIA Halos for Robotics unifies IGX Thor compute and Halos OS to bring full-stack functional safety to Physical AI. By combining an IEC 61508 SIL 3 capable Safety Island with open-source AI safety blueprints, it accelerates compliance and validation for autonomous agents in unstructured environments. 👉 Explore the NVIDIA Isaac platform and scale your robotics development: nvda.ws/3T6vXed 🔹Full Technical Article: nvda.ws/4y4DoTe

See what researchers are building: nvda.ws/4vVk5KO

Open models are becoming the foundation for modern AI research. At #ICML2026, 145 accepted papers cite NVIDIA Nemotron models and datasets, 74 NVIDIA papers were accepted, and ~2,000 accepted papers cite NVIDIA GPUs.