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
HPE ProLiant DL325 Gen11 for AI Workloads
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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
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𝗪𝗵𝗲𝗻 𝘆𝗼𝘂𝗿 𝗰𝗹𝗼𝘂𝗱 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝘀𝗮𝘆𝘀 “𝗚𝗣𝗨 𝘂𝘁𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝟵𝟱%”... 𝗯𝘂𝘁 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱 𝘀𝘁𝗶𝗹𝗹 𝗳𝗲𝗲𝗹𝘀 𝘀𝗹𝘂𝗴𝗴𝗶𝘀𝗵. In our latest blog post, we break down a lesson we learned the hard way running GROMACS molecular dynamics on Google Cloud Dataflow: high-level cloud metrics can look “healthy” even when end-to-end performance isn’t. 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗰𝗼𝘃𝗲𝗿: - Why GPU utilization can be a false comfort (especially for memory-bound workloads) - How we validated cloud monitoring against nvidia-smi / NVML - What we changed to surface the real bottleneck (and what we’re watching next) If you’re building HPC / scientific computing pipelines in the cloud, this could save you real time (and budget). #𝗠𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿𝗗𝘆𝗻𝗮𝗺𝗶𝗰𝘀 #𝗚𝗥𝗢𝗠𝗔𝗖𝗦 #𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 #𝗛𝗣𝗖 #𝗖𝗹𝗼𝘂𝗱𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 #𝗚𝗼𝗼𝗴𝗹𝗲𝗖𝗹𝗼𝘂𝗱 #𝗗𝗮𝘁𝗮𝗳𝗹𝗼𝘄 #𝗚𝗣𝗨 #𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 #𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗕𝗹𝗼𝗴 𝗽𝗼𝘀𝘁: https://lnkd.in/drBWkqkU
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Deka GPU delivers the performance and scalability required to run today’s most demanding AI and ML workloads. With NVIDIA-powered GPU Cloud instances, flexible consumption models, and enterprise-grade security, your teams can accelerate model training, streamline inference, and handle compute-heavy processes with confidence. Deka GPU gives you the expertise and infrastructure to move faster. #EmpoweringYourFuture #LintasartaCloudeka #GPUCloud #GPUMerdeka #CloudSolutions #DigitalTransformation #CloudForEnterprise #ServicePortalCloudeka
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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.
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Intel Xeon 6 on AWS EC2 smashing 20% perf gains over prior Intel gens. 🚀 Cloud peak faster on AWS, IBC optimizing network/EBS. Killer for workloads, but Flex sizing limits huge scale bursts. Flex instances up to 51% lower price for large XL sizes. Netflix throughput boost, Hertz 25% cost cut 💥. Smart variable compute, cold power draw still efficiency drag. Software ecosystem with perf testing plus hybrid Newtonic Cloud? Genius. 📈 Automated opts shine, VMware container AI seamless. Transformative, hybrid mgmt overhead needs streamlining 🤔. Dive into the real summary here: https://lnkd.in/gke-6rMY Follow us and join faikconference.com - tell us how to level up! #AWSreInvent2025 #IntelXeon6 #EC2Flex #CloudTCO 🔥
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Not a server issue. Not a money issue. A spec issue. You don’t need a ₹5-lakh machine to run AI. You need the right memory. In 2026, AI cares less about CPU speed and more about RAM and VRAM. Get that right and AI runs locally. No cloud. No monthly fees. The real mistake? Buying the wrong specs. Matumitha Balavishnu #TechSpecs #HardwareGuide #LocalLLM #LaptopBuyingGuide #TechTips
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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
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CoreWeave’s recent move shows something interesting: "AI-native cloud infrastructure demand is influencing not just tech stacks, but investor sentiment" This mirrors a broader pattern: 1. AI workloads redefine compute needs 2. Hyperscalers and specialized cloud players compete 3. Real-world infrastructure capacity becomes a strategic asset Everyone cares about TB of storage and CPU counts — but real differentiation is happening at the AI compute layer. CPU + SSD is a commodity. AI compute capacity is a competitive moat. If you’re architecting for 2026, this matters more than you think. #CloudPe #AICloud #CloudInvesting #ComputeInfrastructure #TechTrends
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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.
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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
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