Slonk: Simplifying GPU Research Clusters with Kubernetes

Cloud waste is expensive — AI makes compute smarter. AI-driven compute scheduling helps organizations run workloads on the most cost-efficient resources without sacrificing performance or reliability. At JPS Tech Solutions, we help enterprises: ✔ Optimize cloud and cluster resource usage ✔ Reduce compute costs at scale ✔ Maintain performance while lowering spend Because every CPU cycle should count. #JpsTechSolutions #CloudOptimization #ComputeScheduling #EnterpriseAI #CloudEngineering #FinOps #AIAutomation #InfrastructureAI

AI workloads demand more than raw compute. They need storage that can keep GPUs fed at speed. HPE Solutions with WEKA on HPE ProLiant DL325 Gen11 deliver NVIDIA Cloud Partner-qualified, high-performance storage designed for modern AI, ML, and data-intensive workloads. http://tdas.so/018905

Struggling to deploy and scale AI workloads efficiently on the cloud? As AI models grow in complexity and size, managing infrastructure becomes a costly and time-consuming challenge. Fast, reliable, and cost-effective cloud GPU solutions are essential for AI teams to innovate without bottlenecks. Runpod is a cloud platform built specifically for AI, that offers powerful GPUs and seamless scaling to meet your evolving workloads. ✔️ Spin up GPU pods in seconds with cold-start times reduced to milliseconds, so you can start building instantly ✔️ Deploy any container on a secure, global cloud infrastructure supporting public and private repos ✔️ Autoscale AI inference with serverless GPU workers, real-time analytics, and low latency for fluctuating workloads Get Started with Runpod Today - https://lnkd.in/durC9S8r

We got a new Blackwell GPU powered VM from AWS, spot pricing from IBM Cloud and curiously a really good article on spot pricing in clouds too this week. If you already use spot pricing, the article by Alero Awani is definitely worth a read, especially if you want to understand how different CSPs approach their spot offering. If you’re not already using spot you should probably read it so you can start using it! We put out the first of two articles about optimising HPC and AI workload. I get the feeling this year will either result AI providers learning some core lessons in HPC and driving up their utilisation rates to something sensible, or a rather unpleasant reality check in providing heavily subsidized compute. All this and more... see link in comments for the full weekly HPC roundup.

GPUs are no longer a niche accelerator. They’re a foundational layer of enterprise compute. That shift is why GPU-as-a-Service (GPUaaS) is becoming the default model for AI teams moving into production. This post breaks down: 💡 What GPUaaS actually is (beyond “GPUs in the cloud”) 💡 Why traditional infrastructure struggles with GPU workloads 💡 How enterprises use GPU cloud models to improve utilization, agility, and cost control 💡 The operational realities teams need to plan for, from performance to governance If GPUs are starting to shape your delivery timelines, budgets, or architecture decisions, this is worth a read: 🔗 https://lnkd.in/e8s6W7NR

Building AI models that scale safely and efficiently starts with your infrastructure. Private AI deployments give you full control over GPUs, networks, and storage, so you can optimize performance, ensure compliance, and predict costs. For teams handling sensitive data or training large models, hybrid approaches let you combine private clusters with cloud resources without compromise. We’re sharing insights on planning, deploying, and operating private AI infrastructure for high-performance workloads: https://lnkd.in/dBJ7KxGA

Cloud pricing looks predictable until AI starts scaling. A GPU hour sounds simple. But your workload rarely runs entirely on the GPU. You pay for queues. Slow storage. Busy networks. Orchestration delays. All of it keeps the GPU waiting while the meter runs. That's the hidden tax. Performance is blocked by the system around the compute, not the compute itself. Then come the invisible layers. Egress fees. Data movement. Endless API calls. And because you share everything with everyone else, performance changes daily. Yesterday's baseline is today's bottleneck. That's why cloud ROI breaks for AI-heavy workloads. Predictable AI needs predictable infrastructure. Not a lottery.

How to Evaluate & Choose a Cloud GPU Provider in 2026 (A Guide for CTOs) The rapid growth of AI, machine learning and high-performance computing workloads means one thing: GPU infrastructure matters more than ever. But not all GPU providers are built the same. In our latest guide, we cover the key criteria that CTOs and tech leaders should consider before choosing a cloud GPU partner in 2026: 🔹 Performance metrics & benchmarking 🔹 Cost structures that fit enterprise scale 🔹 Security controls & compliance requirements 🔹 SLAs, support levels & integration workflows 🔹 Hybrid and multi-cloud readiness Whether you’re planning GPUaaS, hybrid scaling or edge acceleration, these questions ensure your strategy is future-proof. 👉 Read the full guide here: https://lnkd.in/drcbtpT6 #CloudGPU #GPUaaS #CTO #HighPerformanceCompute #AIInfrastructure #CloudStrategy #DigitalTransformation #ESDS

The modern AI cloud stack isn’t just storage + GPUs. It's also; Dev environments / Distributed training / Optimization / Scalable, high-performance compute. When you are pushing model's harder than ever. Don't you want them pushed at the speed of Lightning AI? Build faster. Train smarter. Deploy confidently.

Qdrant's Edge Over the Competition 🏆 ⚡ Rust-Powered Speed : Blazing-fast queries and reliability that outpaces Pinecone and Weaviate in benchmarks. 🚀 Advanced Filtering : One-stage filtering and hybrid search for precise results, unlike basic competitors. 📌 Flexible Deployment: Open-source, managed cloud, or hybrid—plus GPU acceleration and compliance features. The vector DB built for real-world AI dominance! #Qdrant #VectorDatabase #AIInnovation