Mirantis k0rdent AI Simplifies AI Cloud Setup

👨💻 Read how Rafay turns the NVIDIA Omniverse #DSX Blueprint into a one-click digital twin service: https://lnkd.in/gx3GiyFF 👥 NVIDIA's Omniverse DSX Blueprint shows what an AI factory digital twin can do. 👉 Rafay makes it something you can actually deliver to users at scale. Instead of asking every engineer to configure GPUs, Kubernetes, containers, streaming infrastructure, content packs, and build dependencies, Rafay packages the DSX Blueprint as a governed, one-click service. 🖱️ Users simply: 1️⃣ Select the DSX Digital Twin SKU 2️⃣ Choose a session size and duration 3️⃣ Click launch 4️⃣ Receive a streaming URL Behind the scenes, Rafay provisions the environment, deploys the application, isolates each session, meters usage, and automatically reclaims idle GPU capacity when the session expires. The broader takeaway goes well beyond digital twins. As NVIDIA releases increasingly sophisticated AI and Omniverse blueprints, infrastructure providers do not need to rebuild the application logic. Their opportunity is to make those blueprints repeatable, self-service, multi-tenant, and commercially consumable. ✨ That is the platform layer Rafay provides.

The Trump administration declared that Moonshot's Kimi K3 model was distilled from Anthropic's Fable and further trained on Nvidia's GB300 GPUs in Thailand. According to a report, Moonshot "developed a sophisticated internal platform to conduct large-scale distillation against US models, allowing them to switch between multiple methods of access to avoid detection quickly", with the AI startup already owning some Nvidia GB300 servers and being able to access additional units via Thailand. #AI #Nvidia #Anthropic #Moonshot #AIStartups #ExportControls #TechPolicy #US #TechNews #BusinessNews https://lnkd.in/djrGNvre

Built for Vera Rubin, NVIDIA Spectrum-6 Arrives in Gigascale AI FactoriesAI has entered the gigascale era. The world’s most advanced AI factories are bringing together hundreds of thousands of GPUs and CPUs to train frontier models, power agentic AI and generate intelligence at unprecedented scale. At this level, networking becomes a critical computing power multiplier in driving token generation. Marking a networking milestone, NVIDIA Spectrum-6 […] https://lnkd.in/d2ibYP3P

AMD just threw down the gauntlet. ⚡ At Advancing AI 2026, AMD launched Helios, its first rack-scale AI system and it's not launching quietly. 72 MI455X GPUs, 18 EPYC "Venice" CPUs, and up to 30% more tokens per dollar than the competition. The customer list says it all: OpenAI, Meta, Oracle, Microsoft, and Anthropic are all in. CEO Lisa Su isn't thinking small either she's projecting the AI accelerator market to hit $1.4 trillion by 2030, nearly triple earlier forecasts. Nvidia has owned this space for years. AMD just made it a real fight. #NuPieAnalytics #NuPiePulse #AI #AMD #Nvidia #DataCenter #AIInfrastructure #TechNews #News short

AMD Helios vs NVIDIA Vera Rubin: 7 spec traps in 2027 AI rack quotes: AMD and NVIDIA both ship 72-accelerator AI racks in 2026, and almost none of their headline numbers are measured the same way. A line-by-line normalisation of the two vendor-published spec sheets, and the seven

Everyone talks about the #GPUs driving the #AI revolution... but GPUs can’t build an AI Factory alone It takes a full orchestra of #silicon to keep these massive systems running: #GPUs handle the heavy math. #CPUs orchestrate the workflow. #DPUs secure and virtualize offloads. $NICs move data at top speed. Scale-up & Scale-out #Switches tie millions of nodes together without bottlenecking. While GPUs get all the headlines (and the highest price tag!), the #networking and #switching infrastructure is quietly doing the heavy lifting to keep everything connected. Which chip do you think is the true unsung hero powering today's AI factories?

Everyone is watching model releases. The real signal is the rack. AMD just launched Helios, a rackscale AI architecture for frontier training and inference, and the numbers are hard to ignore. A single rack is listed at: 1. 72 MI455X GPUs 2. 2.9 exaflops FP4 / 1.4 exaflops FP8 3. 31 TB of HBM4 memory 4. Up to 30% more tokens per dollar than the leading competitive solution The strategic part is not just performance. AMD is leaning into open rack standards, Ethernet-aligned networking, ROCm, and partner deployments with OpenAI, Meta, Anthropic, Cerebras and cloud/OEM players. That matters because AI infrastructure is moving from "who has the fastest chip?" to "who can ship usable, open, rack-scale capacity with better token economics?" For builders and operators, the takeaway is simple: GPU choice is becoming a systems decision. Compute, memory, networking, software, power and deployment partners now matter as one stack. Would you treat Helios as a chip story, an infrastructure story, or a serious challenge to AI compute lock-in? #AMD #AIInfrastructure #DataCenter #LLM #OpenSourceAI #ROCm

Armenia is not building chip fabs. It is building compute sovereignty. With NVIDIA GPUs, U.S. approvals, and new AI infrastructure, the real bet is simple: turn imported compute into national advantage. #AI #NVIDIA #ComputeSovereignty #Semiconductors #Armenia

QumulusAI Expands AI Compute Capacity with NVIDIA Blackwell GPUs. Read more on our website: https://lnkd.in/gKfdkCjS QumulusAI has purchased 1,632 NVIDIA Blackwell B300 GPUs to expand its high-performance AI compute capacity and support growing customer demand. The expansion includes 204 NVIDIA HGX B300 systems and additional NVIDIA RTX PRO 6000 Blackwell GPUs to support AI training, inference, and visualization workloads. Mike Maniscalco, CEO of QumulusAI, highlighted the company’s focus on delivering flexible and scalable AI infrastructure at speed. #AIInfrastructure #NVIDIA #ArtificialIntelligence #CloudComputing #ITTechPulse #BestPlaceToWork