One rack. 1.64 petabits per second. @CoreWeave shares what's possible when compute, networking, and cooling are built as one system with NVIDIA Vera Rubin NVL72 and NVIDIA Spectrum-X Ethernet with Spectrum-6 102.4T switches. Learn more 👇

🤝 @arcee_ai, an American AI lab, built its frontier open model family on NVIDIA Blackwell Ultra. Its flagship MoE model, Trinity-Large-Thinking, was RL post-trained using NVIDIA NeMo open libraries. Arcee optimized agentic model inference with NVIDIA Dynamo and vLLM, along with NVIDIA accelerated networking, to deliver low token cost, achieving: ✅ 3T+ tokens served on OpenRouter in its first two months ✅ $0.90 per 1M output tokens ✅ 2nd on PinchBench for open agentic models Explore Arcee's open models, powered by NVIDIA ➡️

The NVIDIA Vera Rubin NVL72 compute tray. 200 AI petaFLOPs. Assembled in one minute. ​ That's what a single-wide, third-generation NVIDIA MGX rack enables. No cables. No hoses. No fans. ​ It's 100% liquid cooled at 45°C and brings together Vera Rubin Superchips, ConnectX-9 SuperNICs, and BlueField-4 DPUs to deliver the lowest token cost and best performance per watt.​ #NVIDIAVeraRubin#

📣 An #NVIDIADGX# GB300 system will become operational at @NPS_Monterey this month. To enrich AI skills for officers and students in the public sector, DGX GB300 will support the development of AI applications and address operational challenges in fields ranging from weather modeling, oceanic and operations research, to disaster resilience and response planning. 💡 Read more on how we’re training the next generation of AI leaders with NPS: 🔗 Learn more about the NPS and NVIDIA collaboration at Converge @ NPS:

💡 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.

Up to 10x better inference per watt. One-tenth the cost per million tokens. This is what NVIDIA Vera Rubin NVL72 delivers. Join us for the virtual event on June 30 as Dion Harris, Senior Director of HPC and AI Hyperscale Infrastructure Solutions at NVIDIA, and Harsh Banwait, Director of Product at @CoreWeave, explain what it means for the agentic era. #theCUBE# 📆 Save the date:

👏 Congratulations on the GA, @awscloud! Amazon EC2 G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs deliver: ✅ Up to 4.6x AI inference performance ✅ Up to 2.1x graphics performance ✅ Significantly faster GPU-accelerated data analytics on Amazon EMR using NVIDIA cuDF for Apache Spark workloads. 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:

AI Supercomputing 🤝 EDA Join Tim Costa at @DACconference to learn how collaborating with AI at scale can unlock breakthrough performance, faster innovation, and reshape the future of chip design. #DAC2026# 📅 July 26, 2026 📍 Long Beach Convention Center 🔗 Learn more:

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:

🎥 Live from #PureAccelerate#! AI projects don't fail because of models—they stall because of data. That's why Everpure is announcing the availability of Everpure Data Stream, built on the @nvidia AI Data Platform reference design to transform unstructured data into real-time AI results while reducing cost and complexity. Tune in to Everpure's Shawn Rosemarin and NVIDIA's Kevin Deierling discuss what's next for enterprise AI and the data foundation needed to make it work. #EverpureData# #AI# #EnterpriseAI# #DataManagement#

Water usage has been a hot topic in the AI data center world, but the numbers may surprise you. According to the Manhattan Institute, data centers use 0.2 percent of daily water usage in the U.S. and that number has dramatically decreased in the past few years due to a new method: liquid cooling. By moving to 45°C liquid cooling, AI factories in favorable climates can use dry coolers instead of conventional cooling-tower-based systems, cutting facility cooling water use from roughly 2.6M gallons per MW per year to near zero. Liquid cooling enables AI factories to be both water and energy efficient, while creating opportunities for heat reuse and dispersal to local communities, allowing these factories to become energy grid assets. Learn more below ⬇️

Production AI demands infrastructure that holds up at scale. NVIDIA and @AWSCloud announce three advances that strengthen every layer of the AI infrastructure stack. 1️⃣ AWS EC2 G7 instances bring NVIDIA Blackwell GPUs to AWS, delivering up to 4.6x AI inference performance over G6 2️⃣ Amazon OpenSearch Serverless NextGen now uses GPU-accelerated vector indexing powered by NVIDIA cuVS as default, enabling vector indexing up to 10x faster at a quarter of the cost 3️⃣ AWS Achieves NVIDIA Exemplar Cloud Status for GB300 Training Performance 🔗

We're now the #1# vendor by revenue in datacenter Ethernet switching, according to @IDC. NVIDIA Spectrum-X Ethernet has captured hyperscaler and enterprise demand for AI factory networking, driven by co-design across GPUs and Ethernet switching infrastructure. 📰 Learn more:

The latest supercomputing rankings are in - NVIDIA is powering 81% of the TOP500 and 89% of all new systems, in addition to: ⚡2x the AI training throughput of all other platforms combined ⚡3x the AI inference throughput ⚡The top 8 most energy-efficient systems on the Green500 The world’s AI infrastructure runs on NVIDIA. #ISC26# @Top500supercomp #Top500# #Green500#

Read the announcements: Aegiq: Quantum Motion: Classiq: FirstQFM: Fraunhofer FOKUS: Qilimanjaro: Qbraid: QCentroid: Welinq:

Scaling hybrid workloads with NVIDIA CUDA-Q is enabling teams across the quantum ecosystem to explore real-world impact using GPU-accelerated simulation. 🚀 ⚡ Aegiq and @Quantum_Motion are advancing quantum chemistry workflows with CUDA-Q. ⚡ Classiq is drawing on CUDA-Q to explore new quantum applications in finance. ⚡ FirstQFM has demonstrated quantum foundation models on the Leonardo supercomputer. ⚡ Eclipse Qrisp (pioneered by @Fraunhofer FOKUS) and @Qilimanjaro are powering their work with CUDA-Q. ⚡ qBraid now serves as a CUDA-Q target, expanding access to a broad set of QPU providers. ⚡ QCentroid is building QuantumOps workflows on CUDA-Q, driving more efficient applications development. ⚡ Welinq is pairing its distributed quantum compiler with CUDA-Q for GPU-accelerated circuit verification. The path to useful quantum computing is hybrid—powered by accelerated simulation. Learn more: #ISC26#

Introducing the CPU built for the AI era — NVIDIA Vera. Los Alamos National Laboratory is powering its next generation of supercomputers — Mission, Vision, and Veritas — with NVIDIA Vera CPUs at their core. These systems are purpose-built to support agentic AI: autonomous systems that can reason, simulate, and accelerate scientific discovery in ways never before possible. From materials simulation to molecular design, NVIDIA Vera CPU is what's making it happen. #ISC26# 🔗

This is what exascale science looks like. 🌍🧠📶 Europe's first exascale supercomputer, JUPITER, is officially in production. Powered by NVIDIA Grace Hopper, it’s already tackling challenges that were once completely out of reach: Mapping the human brain at a cellular scale in under five days. Simulating the entire Earth’s climate at an unprecedented 1-km resolution. Laying the AI groundwork for the next generation of 6G wireless networks. Discover how NVIDIA and JUPITER are redefining the frontier of scientific discovery. #ISC26# 👉 Read the full story:

State of AI compute 2026: my conversation with @stephenbalaban of @LambdaAPI on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI