Aleksander Šinigoj’s Post

The new IBM and Groq partnership tackles AI’s biggest barrier, efficiency. Many organizations can build powerful AI pilots, but running them reliably and affordably at scale is another challenge. Inference is compute-heavy, latency slows automation, and without the right infrastructure, intelligent agents rarely make it into production. By integrating Groq’s high-speed LPU architecture and GroqCloud inference technology into IBM’s watsonx Orchestrate, enterprises can now achieve 5× faster AI performance at roughly 20% of the cost, all within IBM’s standards for security, governance, and trust. IBM has seen this impact firsthand. As our own Client Zero, we’ve embedded AI into operations to deliver over $3 billion in annual savings. The takeaway is clear: when AI runs efficiently and securely, it delivers measurable business results. Together, IBM and Groq are solving for the fundamentals that determine whether AI can truly scale: ⚡ Speed: AI that acts in real time. 💰 Efficiency: performance that scales without runaway compute costs. 🛡️ Trust: governance designed for complex, regulated environments. Together, IBM and Groq help enterprises move from promise to production with enterprise AI that is fast, intelligent, and built for real-world impact. #AI #watsonx #Groq #HybridCloud #ClientZero #EnterpriseAI #Efficiency #Governance #IBM

As AI adoption accelerates, many enterprises face a hidden challenge: network congestion that limits GPU utilization, slows training, and impacts real-time inference. In our latest article, we share what we learned after implementing large-scale AI clusters, and how your organization can apply these lessons right away. Discover how to: → Define and diagnose AI workload requirements → Implement ECN & PFC for real-time congestion control → Deploy dynamic load balancing for smoother data flow → Reduce latency and accelerate training by up to 30% Read the full guide to see how your data center can scale AI workloads efficiently, without costly network bottlenecks: 'https://lnkd.in/d8BmfjRG' #AIInfrastructure #DataCenters #Networking #MachineLearning #ArtificialIntelligence #MLOps #HPC #NetworkOptimization #GPUComputing #EnterpriseIT #CloudInfrastructure #TechLeadership #PerformanceEngineering #AIDevelopment

Sovereign AI Isn't Just About Data Residency. It's About Performance Sovereignty. The world is racing to build sovereign AI clouds. But simply having local data centers isn't enough. True sovereignty means having control over the entire stack—from the silicon to the model inference. For the past 20 years, my journey has taken me from the fundamentals of server performance benchmarking to the frontiers of Agentic AI. This has given me a core belief:  The next competitive battleground is at the intersection of HPC principles and AI workload orchestration. We're moving beyond just choosing a model. We're now architecting systems where: Specialized silicon (GPUs, NPUs, TPUs) must be fully utilized to manage TCO. HPC-class networking is non-negotiable for distributed training and massive inference. "Slim" models will be orchestrated into powerful, cost-effective agent swarms, demanding a fundamentally new infrastructure layer. The 200-megawatt data centers being built today are not just power hubs; they are the engines of a nation's economic future. Their success won't be measured in petaflops alone, but in FLOPS per watt per dollar of real-world business value delivered. This is the new playbook: Hardware-aware AI optimization at scale. It's the convergence of my life's work, and the key to making sovereign AI not just sovereign, but superior. I'll be sharing more insights on the architectural patterns making this possible. What do you see as the biggest infrastructure challenge for sovereign AI? #SovereignAI #HPC #AIInfrastructure #GPU #PerformanceEngineering #Core42 #DigitalEconomy

The AI Resilience Gap Is Real — And It's Costing Millions Everyone’s talking about AI performance. But no one’s talking about AI resilience. We’ve spent years perfecting algorithms and GPUs… 💥 Yet projects are failing for one simple reason — the data can’t keep up. Gartner says 60% of AI projects will be abandoned by 2026 if they’re not supported by AI-ready data. That’s not a model issue. That’s a resilience issue. 🧠 The problem: AI workloads are only as strong as the infrastructure feeding them. When data pipelines break or storage slows, training halts, GPUs sit idle, and ROI vanishes. 💸 The cost: Poor data quality = $12.9–$15M lost annually per enterprise Data pipeline failures = $300K/hour in lost insights Every delay = wasted compute + delayed deployment + frustrated teams 💡 The fix: Replace fragile legacy storage with hybrid or all-flash architectures Use multi-level erasure coding (MLEC) to protect against simultaneous device failures Embed automated data integrity checks and regular recovery drills into your AI ops Treat resilience as a continuous discipline, not a post-mortem AI’s future performance isn’t limited by algorithms — it’s limited by how fast and resilient your data systems can recover. If resilience isn’t in your AI strategy, you don’t have one. 👇 How is your organization closing the AI resilience gap? #BusinessContinuity #AIResilience #CIO #DisasterRecovery #HybridStorage #AITransformation #DataManagement #ResilientAI #AIOps #Infrastructure #RiskManagement #DigitalTransformation #BCM #DataIntegrity #TechStrategy #EnterpriseIT #CIOConsultant #OperationalResilience #AIInfrastructure #ResilienceEngineering

Exciting News: MCP Servers Just Got Smarter! 🤖 We’re thrilled to announce the latest updates to MCP Servers, designed to supercharge AI workloads and empower developers, researchers, and enterprises to do more — faster, smarter, and at scale. Here’s what’s new: ⚙️ Optimized Compute Performance – Enhanced parallel processing for AI and ML training, inference, and data analytics. 🌐 Intelligent Load Balancing – Dynamic resource allocation to maximize efficiency and minimize downtime. 🔒 Advanced Security Layer – Upgraded encryption and adaptive access controls for safer AI deployments. 💡 AI-Native Monitoring Tools – Real-time insights, predictive diagnostics, and automated scaling powered by machine learning. These upgrades mean faster innovation cycles, reduced operational overhead, and a better foundation for deploying next-gen AI systems. Whether you’re building AI products, training models, or managing data pipelines — MCP Servers now make it easier than ever to bring your AI vision to life. #AI #MachineLearning #MCPServers #CloudComputing #Innovation #TechUpdate #Infrastructure

AI's insatiable demand for data is reshaping enterprise storage, pushing organizations to prioritize speed, resilience, and scalability. From hybrid systems to advanced data durability, adapting storage infrastructure is key to unlocking AI's full potential. Is your storage ready for the AI era? #AI #DataStorage #TechInnovation  

Fantastic conversation. Ranjan does a great job explaining how the real bottleneck for AI at enterprise scale isn’t just algorithms, it’s infrastructure. The move toward agentic and generative systems is forcing every enterprise to rethink data, compute, and governance as first-class strategy, not IT support. IBM’s focus on hybrid architectures and open systems is a clear signal that the future of AI will be built on flexibility, trust, and scale.

AI and Data: Oracle’s Big Bet Pays Off Oracle’s new AI Data Platform keeps dominating headlines. Enterprises from finance to health are investing to unify their data, build generative apps, and (hopefully) stop calling every spreadsheet “AI.” It’s like transforming your dusty filing cabinet into a digital brain - with security clearance and a GPU. What can we take out of it? ✅ Unified Intelligence – Data ingestion, vector indexing, and agentic automation in one hub. ✅ Enterprise‑Ready – Integrated NVIDIA acceleration for production‑grade workloads. ✅ Actionable AI – Automates insights into workflows across business lines. ❌ Watch Out – Centralized data without governance equals faster mistakes and bigger headlines. The mission is clear - AI success isn’t about collecting data; it’s about connecting trust. Are your teams spending more time feeding data - or feeding insight? #AI #DataPlatform #Oracle #DigitalTransformation #ResponsibleAI

AI is crushing legacy storage. By 2029 we'll need ~2 exabytes more-and today's stacks weren't built for it. As a recent Computer Science graduate, I've seen firsthand how traditional storage systems struggle with modern AI workloads. The numbers are staggering. Gartner predicts storage demand from generative AI will jump from under 1 exabyte in 2024 to over 2 exabytes by 2029. Legacy systems face three critical challenges: • Performance bottlenecks that slow AI training • Scalability issues for massive datasets • Poor metadata management for AI workflows HPE's new Alletra Storage MP X10000 tackles these head-on. The solution offers: • 6x faster performance than competitors • All-NVMe architecture for speed • Disaggregated design for independent scaling • Native integration with NVIDIA's AI platform What excites me most is the focus on Retrieval Augmented Generation pipelines. This technology will be crucial as organizations race to implement AI solutions. The storage industry is at an inflection point. Companies that adapt now will have a significant advantage in the AI revolution. Are you seeing storage bottlenecks in your AI projects? How are you planning for the massive data requirements ahead? #AI #DataStorage #TechInnovation 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gVsKBdi9

��Architecting Enterprise AI for Generative and Agentic Systems - with Ranjan Sinha, Ph.D. Sinha of IBM” https://lnkd.in/d_MacVm On the latest episode of the ‘#AIinBusiness’ podcast, Ranjan Sinha, Ph.D. Sinha, IBM Fellow and Chief Technology Officer for watsonx and IBM Research, joins Emerj Artificial Intelligence Research Editorial Director Matthew DeMello to explore how infrastructure has become the defining factor in enterprise AI transformation. As organizations move from experimentation to production-scale systems, Ranjan explains why the success of agentic and generative AI depends on rethinking the foundations of compute, governance, and data management. Ranjan details how IBM is advancing AI performance through breakthroughs in neuromorphic and quantum computing, including the NorthPole chip, which delivers unprecedented efficiency, and IBM’s roadmap toward fault-tolerant quantum systems by 2029. He also outlines IBM’s commitment to open strategies and hybrid architectures, showing how watsonx and Red Hat enable enterprises to scale AI securely and responsibly while maintaining innovation velocity. Their conversation underscores how infrastructure is no longer a back-office function but the backbone of AI-driven business value. From reimagining computational architectures to embedding trust and governance across the stack, IBM’s approach reflects a new era of scalable, enterprise-ready AI. #EnterpriseAI #IBMAI #watsonx #HybridCloud #QuantumComputing #AIinfrastructure #AgenticAI #AIgovernance #NeuromorphicComputing #OpenSourceAI #AIadoption #AIinnovation #DigitalTransformation #Emerj