GPU Portability for Production AI with Launch Templates

🖱️ Manual Fix vs. 🤖 Cast AI 𝘼𝙪𝙩𝙤𝙢𝙖𝙩𝙞𝙤𝙣 : The autonomous engine for modern app & AI performance Cast AI’s autonomous agents take real action in production: rightsizing CPU, memory, and GPU, scaling nodes on demand, and keeping cloud-native and AI environments healthy. Anywhere you run Kubernetes. #Kubernetes #SRE #Cloud #AI #GPU

🚀 Rent or Buy GPUs for AI Projects? This question usually pops up after the 1st successful pilot. Cloud GPUs look attractive. And for good reason: • No upfront capex • Instant provisioning • Easy experimentation But once workloads become predictable, the economics shift. In practice, the most rational setup I’ve seen is hybrid: ✔ Own GPUs for stable, baseline workloads ✔ Rent for experimentation and peak loads ✔ Keep architecture portable (so you’re not locked into one vendor) 👉 The real mistake isn’t renting or buying. It’s making the decision without modeling: • Utilization rate • Growth curve • Latency requirements • Operational overhead If you're evaluating long-term AI infrastructure strategy, I’m happy to compare notes. A 15-min conversation prevents a 6-figure architectural regret! #AIInfrastructure #CloudStrategy #MLOps #GPU #Scaling

🚀 Serverless AI Just Got a Massive Upgrade The new NVIDIA RTX PRO 6000 Blackwell GPU on Google Cloud Run is a game changer for AI workloads. With 96GB vGPU memory, 1.6 TB/s bandwidth, and FP4/FP6 precision support, you can now run and serve 70B+ parameter models — without managing any infrastructure. What makes it powerful? ⚡ Go from zero to GPU in under 5 seconds 📉 Automatic scale-to-zero when idle 🧠 High-efficiency inference for Generative AI 🎨 Real-time multimodal & text-to-image applications 🔧 No reservations, no manual driver setup This means you can focus purely on building intelligent applications — not provisioning servers. For startups and enterprises building AI-first products, this unlocks: Faster experimentation Lower idle cost Production-ready scalability True serverless GPU acceleration The future of AI deployment is simple: On-demand. Scalable. Serverless. #GenerativeAI #CloudRun #NVIDIA #Blackwell #Serverless #AIInfrastructure #LLM #GoogleCloud

How do we extend Kubernetes to support GPU-backed LLMs and other AI workloads? I'll show you in this video! 🤩 Third one in the series on Kubernetes + AI, this video covers how we can advertise GPU devices in our nodes so that a pod can request them as resources. This is that last big step before deploying your own LLM in your Kubernetes cluster! 🥳 You don't have to watch the first two videos that talk about installing the GPU device and scaffolding out VMs in a hypervisor. If you want to use a public cloud that does all that for you, this video has you covered! I'll show you how it's done in EKS, covering the before and after of installing the Nvidia GPU Operator. Took a little longer to get this one out than I would have liked, but Mardi Gras puts all other aspects of real life on hold. 💜 💚 💛 👉 [link: https://lnkd.in/esRbmQ77] #kubernetes #eks #k3s #nvidia #gpu #llm #ai

Lightning started with a simple idea: remove friction from AI development. Now Lightning AI has added Voltage Park’s hardware to form the only full stack cloud for enterprises. 36K+ NVIDIA GPUs for training and inference means: No more juggling vendors. No more waiting on compute. No gap between research and production. If you’re at GTC, stop by Lightning AI booth 1131 and tell us what you’re working on, or message me to book some time.

VS Code Extensions Enable Local LLM-Powered Development Workflows 📌 Local AI coding tools now let developers run large language models directly in VSCode-no cloud, no telemetry. Extensions like RooCode and KiloCode offer full AI assistance with privacy, while Vulkan-backed performance on AMD GPUs boosts speed and cuts power use. A new era of private, powerful, and efficient local development is here. 🔗 Read more: https://lnkd.in/dHZrs7Pq #Roocode #Kilocode #Cline #Localllm #Vscodeextensions

Excited to share that NVIDIA's Blackwell Ultra platform is transforming the economics of AI inference. Our GB300 NVL72 systems deliver up to 50x higher throughput per megawatt and 35x lower cost per token compared to Hopper, enabling a new generation of real-time agentic AI applications. Major cloud providers including Azure, CoreWeave, and OCI are already deploying this breakthrough technology for next-gen coding assistants and agentic workflows. Check it out!

Stop managing complex GPU clusters 🤯 for your AI projects. Did you know you can run EmbeddingGemma on Cloud Run with L4 GPUs in a serverless environment? My new lab shows a setup which allows you to build semantic search and RAG systems without the infrastructure overhead, so you only pay for what you use. You'll learn how to: - Containerize Ollama and EmbeddingGemma models. - Deploy to Cloud Run with NVIDIA L4 GPU acceleration. - Generate text embeddings via API. - Build a functional semantic search app with ChromaDB. The guide takes ~40 minutes to complete and provides a scalable foundation for any application requiring text understanding. 🚀 Link in the comments 👇 #GoogleCloud #CloudRun #Gemma #AI #Serverless

The power of 10,000 GPUs at your fingertips. ✨From #AI strategy to real-world impact like never before. The Industrial AI Cloud is designed for organizations that need: • Massive GPU power at scale • Predictable, low-latency inference • Secure, sovereign AI infrastructure With access to 10,000 NVIDIA Blackwell GPUs, European enterprises can train and operate their own AI models without long lead times or dependency on non-European hyperscalers. This is an AI infrastructure built for speed, scale, and trust. Explore more here: 👉 http://ms.spr.ly/6045QicoT #EnterpriseAI #AIInfrastructure #DigitalTransformation #DeutscheTelekom #TSystems