Edge Computing in Networks

For years the data center industry chased bigger. Bigger campuses. Bigger power contracts. 1,000-MW mega facilities. But the AI era is exposing a flaw in that model. AI inference doesn’t want to live 1,000 miles away. When decisions must happen in milliseconds — for power grids, public safety, robotics, financial systems, or smart cities — sending data to a distant hyperscale cloud and waiting for it to come back simply doesn’t work. So the architecture is changing. Instead of one massive campus: • 1,000 smaller urban sites • Compute next to where data is created • AI inference at the edge • Capacity that can scale in weeks, not years That’s the idea behind distributed AI infrastructure. Projects like Project Qestrel are rolling out fleets of edge data centers across U.S. cities — bringing HPC and AI inference directly into metro networks. Hyperscale isn’t going away. But the future of AI won’t be one giant brain in the desert. It will be a nervous system of distributed intelligence. And the closer compute gets to the edge, the faster the world gets. #EdgeComputing #AIInfrastructure #DataCenters #AIInference

"ARM CPUs + Apache Kafka = A Perfect Match for Edge AND Cloud" Real-time #datastreaming is no longer limited to powerful servers in central data centers. With the rise of energy-efficient #ARM CPUs, organizations are deploying #ApacheKafka in #edgecomputing, in addition to the widespread hybrid #cloud environments—unlocking new levels of scalability, flexibility, and sustainability. In my blog post, I explore how ARM-based infrastructure—like #AWSGraviton or industrial IoT gateways—pairs with #eventdrivenarchitecture to power use cases across #manufacturing, #retail, #telco, #smartcities, and more. ARM CPUs bring clear benefits to the world of #streamprocessing: - High energy efficiency and low cost - Compact form factors ideal for disconnected edge environments - Strong performance for modern #IoT and #AI workloads The combination of Kafka and ARM enables more cost-efficient and sustainable applications such as: - Predictive maintenance on the factory floor - Offline vehicle telemetry in #transportation and #logistics - Local compliance automation in #healthcare - In-store analytics and loyalty systems in food and retail chains Read the full post with use cases, architecture diagrams, and tips for building cost-effective, resilient, real-time systems at the edge and in the cloud: https://lnkd.in/eeJ6mcaH

If cloud computing gave us flexibility, edge computing is giving us speed—and that's the real game-changer. As someone who's helped businesses rethink their tech strategy, I see this shift everywhere: from manufacturing to healthcare, the need for real-time decisions is redefining how we process data. Edge computing doesn’t replace the cloud—it complements it. By processing data closer to where it's generated, edge computing cuts latency, improves reliability, and makes true real-time action possible. Here’s how edge is already making an impact: 🚗 Self-Driving Cars → They can’t wait for cloud responses. On-board systems make split-second decisions to ensure safety. 🏭 Smart Factories → Machines detect issues and adjust instantly, avoiding accidents and reducing downtime. ❤️ Healthcare Devices → Wearables and monitors respond in real time, giving doctors live insights that save lives. 🛒 Retail Innovation → AI-powered cameras and sensors adjust digital signage, pricing, or promotions in the moment based on who’s shopping. In other words, edge is where data meets action. Instantly. Pro tip: As companies grow more connected, a hybrid model—cloud + edge—is the future. Use the cloud for storage and heavy analytics, and edge for the urgent, real-time stuff. In my experience, making the right call about where to process data is becoming just as important as what you process. Curious to hear from you: where do you see real-time processing having the biggest impact in your industry? Drop your thoughts in the comments. And if you’re into tech, strategy, and future-ready ideas, follow me for more. #EdgeComputing #CloudComputing #IoT

Navigating IoT architecture can feel like a maze, especially when deciding where to process your data. I've seen many teams struggle with this choice, leading to costly redesigns if the wrong layer is prioritized. To avoid those pitfalls, here’s a straightforward breakdown to help you choose the right architecture for your next build: ➞ Cloud Computing Centralized processing in remote data centers. Great for massive data analytics, long-term storage, and training complex AI models at scale. Think infinite capacity, but be mindful of latency and bandwidth costs. ➞ Edge Computing Processes data directly on IoT/edge devices. This means ultra-low latency, minimal bandwidth use, and robust offline capability. Ideal for critical, real-time decisions needed in wearables, factory robotics, and cameras. ➞ Fog Computing Sits between the cloud and the edge, processing data closer to the source via gateways. Delivers near real-time response, enhanced security, and intelligent filtering before data ever hits the cloud. Used extensively in smart cities, healthcare hubs, and industrial automation where localized intelligence is key. The key takeaway? It's not about picking just one. The real win in IoT is strategically orchestrating capabilities across Cloud, Edge, and Fog tiers to balance speed, cost, intelligence, and reliability. Ignoring any layer can create significant challenges down the line. 🔁 Repost if you're building for the real world, not just connected demos. ➕ Follow me, Nick Tudor, for more insights on AI + IoT that actually ship.

🚨 AI Infrastructure Just Moved Into the Backyard 🏡⚡🤖 This might be one of the most disruptive shifts we’ve seen yet in the AI infrastructure space. SPAN just announced XFRA — a distributed AI platform that places liquid-cooled GPU nodes at residential homes to deliver inference compute directly at the edge of the grid. Yes… actual homes. 📊 What’s happening: 🏡 Outdoor AI compute modules installed alongside SPAN smart electrical panels ⚡ Leveraging unused residential power capacity (~60% headroom on average) 🧠 Each node packed with 16 NVIDIA Blackwell GPUs + CPUs + 3TB memory 🔋 Battery-backed for resilience (home + compute) 🌐 Designed for low-latency inference, cloud gaming, and edge AI workloads 🚀 100-node pilot this year → gigawatt-scale deployment target by 2027 📌 The Big Idea: ⚡ Residential electrical systems are built for peak demand… but rarely use it 💡 That leaves massive untapped capacity sitting idle across millions of homes SPAN is turning that into distributed compute infrastructure. And it directly tackles the biggest constraint in AI today: ⏱️ Speed-to-power Instead of waiting years for new transmission, substations, and hyperscale builds… ⚡ Use what already exists 🏗️ Deploy in months 📍 Put compute exactly where it’s needed 📌 Why This Matters (Zoom Out): We’re seeing a clear pattern emerge across the industry: 🏢 Hyperscale campuses → still critical for training 🏗️ 20–50MW modular sites → scaling inference regionally 🏡 Now → sub-5MW and even residential nodes entering the mix This is the logical extension of distributed AI: 🧠 From centralized → distributed → hyper-distributed 📍 From cloud → edge → grid edge → home edge And it changes the role of infrastructure entirely: ⚡ The home is no longer just a load 🔌 It becomes part of the compute network 🌐 And potentially part of the grid solution 💬 “Distributed compute is the next logical extension of our technology.” — SPAN CEO Arch Rao 💬 “There is a critical need for low-latency solutions that are proximal to end users and can scale rapidly.” — NVIDIA Read the Article: https://lnkd.in/d6mBYUDW 💬 Question for the industry: If AI compute can scale using millions of small nodes instead of a few giant campuses… 👉 What does that mean for utilities, infrastructure planning, and the future of the grid? #AIInfrastructure #EdgeComputing #DistributedAI #DataCenters

The next AI bottleneck isn’t compute. It’s the network. Everyone is racing to build bigger AI models and larger GPU clusters but the real constraint in the Era of Inference is something far less visible. Latency. In centralized AI deployments, 20–40% of end-user latency comes from network bottlenecks such as jitter, packet loss, and inefficient routing. That problem compounds rapidly as AI shifts toward: • Real-time inference • Agent-to-agent communication • Autonomous systems • Industrial AI workloads The economic impact is significant. By 2030, the cost of network latency in AI systems is expected to exceed $80 billion globally, while the broader AI inference market approaches $255 billion. This is where a new architecture is emerging. AI will move to the edge. And one of the most underappreciated infrastructure assets sits in plain sight. Rural fiber networks. Across North America, rural broadband operators operate high-capacity fiber networks with: • Power availability • Proximity to renewable energy • Available land for micro-datacenters • Rapid deployment timelines These networks can host distributed AI inference infrastructure in months instead of years. But distributed inference requires something critical: An optimized transport layer. FGN’s network technology focuses on solving the exact problem that limits AI performance today: • Reducing jitter and packet loss across long routes • Maintaining sub-50ms latency targets • Enabling split inference between edge encoders and cloud decoders • Accelerating agent-to-agent workflows by 30–60% The result is a new type of AI infrastructure stack: Compute + Edge + Network Optimization Not just bigger datacenters. Smarter transport. The opportunity is clear. AI inference is becoming a network problem, not just a compute problem. And the operators who understand that shift early will define the next decade of infrastructure. If you are building AI infrastructure, broadband networks, or edge compute, the question is simple: How close is AI to your users?

🩺 What if rural clinics could run AI diagnostics… without the internet? Sounds impossible? It’s not. It’s called edge infrastructure — and it might be the biggest game changer the health sector has seen in decades. Here’s the reality: Most AI in healthcare today relies on cloud infrastructure — which means it’s expensive, data-heavy, and completely reliant on stable internet. 🌍 In cities? Maybe. 🏥 In rural clinics? Good luck. But with edge technology, something radical happens: ✅ AI runs locally — right on-site ✅ No need for constant internet ✅ Real-time processing of images, feedback, or patient triage ✅ Massive cost reduction ✅ More access to care for more people Imagine this: 📡 A clinic with no broadband, but still able to: Run visual diagnostics for maternal health Triage patients automatically during high-volume hours Store and sync health records safely — even offline That’s not a distant future. That’s edge computing, done right. 💡 If we’re serious about affordable, scalable healthcare, then edge infrastructure isn’t optional — it’s essential. We can’t wait for connectivity to catch up. We need to bring intelligence to the last mile — today. Agree? Have a use case in mind? I’d love to hear it.👇 #HealthTech #EdgeComputing #DigitalHealth #AIinHealthcare #SmartClinics #LastMileInnovation #AfricaHealthTech #OfflineAI #InfrastructureInnovation #TechForGood

Nvidia Backs Nokia. SoftBank Exits Nvidia. The Next AI Wave Is Inference at the Edge. And Telcos are Key. Some interesting signals in the AI world these past two weeks. First, NVIDIA invested $1B into Nokia to build AI-native radio networks — and has already said it wants to do similar deals with Ericsson and other RAN vendors. A week later, Softbank sold all $5.8B of its Nvidia stake — not to exit AI, but to redeploy capital into the next wave of AI opportunities. Two very different moves, but pointing to the same underlying shift. According to new research from Analysys Mason, the next phase of AI won’t be defined by massive training clusters. The training era is maturing. The real growth — over 60% of GPUaaS revenue by 2030 — will come from inference: running AI in production, close to users, data, and real-world systems. Inference needs proximity. Inference needs sovereignty. Inference needs predictable latency and network control. Which suddenly puts telcos and distributed network infrastructure right in the spotlight. Nvidia’s interest in Nokia — and soon Ericsson and others — is a clear sign that intelligence is moving into the network itself: from RAN → fiber → MEC → enterprise edge. And that’s where telcos already have unique advantages: footprint, trust, compliance, and local presence. Even Network APIs — which have been searching for a breakout use case — may find it in edge AI. Identity, consent, QoS, routing, network awareness… This is exactly what GSMA Open Gateway was designed to enable. Meanwhile, players like Intel Corporation (with its production-grade inference stack and optimized enterprise models) and e& enterprise (rolling out edge-AI services across the region) are already showing what this next layer looks like in practice. And more telcos rolling out similar solutions. It feels like we’re entering the “real economy” phase of AI: Less hype. More deployment. Less centralised. More distributed. Less focus on training. More on making AI actually work inside networks and enterprises. If you’re looking for some weekend reading, I wrote a longer piece about this shift — and why this moment may be bigger for telcos than it appears: https://lnkd.in/eFZK-Z7h Happy Friday! #AI #EdgeAI #Inference #Telecom #NetworkAI #Nvidia #Nokia #Ericsson #OpenGateway #NetworkAPIs #SovereignAI #FutureofNetworks SoftBank Investment Advisers Mark Castleman Hakim Annaciri Ivan Ostojic Selenga Akiner Louis Powell Justin Paul Lily Cheng Henry Calvert GSMA GSMA Open Gateway CPaaS Acceleration Alliance

𝗪𝗲 𝗖𝗮𝗻'𝘁 𝗣𝗿𝗲𝗱𝗶𝗰𝘁 𝗪𝗵𝗲𝗿𝗲 𝗘𝗱𝗴𝗲 𝗔𝗜 𝗪𝗶𝗹𝗹 𝗥𝘂𝗻 - 𝗕𝘂𝘁 𝗪𝗲 𝗖𝗮𝗻 𝗕𝗲 𝗥𝗲𝗮𝗱𝘆 The telecom industry is asking: where will AI workloads actually run? Network edge? Enterprise edge? Distributed across both? The honest answer: it depends on the workload. 𝗧𝗵𝗲 𝗙𝗼𝘂𝗿 𝗙𝗼𝗿𝗰𝗲𝘀 𝗧𝗵𝗮𝘁 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 Workloads ultimately migrate to where they run "best." And "best" is determined by each workload's unique needs across four dimensions: - Performance – Latency, throughput, processing requirements - Economics – Infrastructure costs, operational costs, total cost of ownership - Security – Data sovereignty, regulatory compliance, privacy requirements - Data Gravity – Both data consumed (for processing) and data produced (as output) These four forces explained the public cloud migration wave and why some workloads repatriated. They explain why workloads are emerging at the enterprise edge. And they'll determine where edge AI workloads land. 𝗪𝗵𝘆 𝗔𝗜 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗔𝗿𝗲𝗻'𝘁 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 AI inferencing will follow data. It won't centralize—it will distribute to wherever data lives and decisions need to be made. A real-time safety application has completely different requirements than a demand forecasting model. Different workloads, different optimal locations. AI training might be centralized (different workload characteristics), but the majority of inferencing will be distributed—hence the understandable interest in this question from network operators. 𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 Here's what we actually know: you can't bet on a single location for all edge AI infrastructure without understanding the edge AI workload. But you can bet on this: - legacy, closed, appliance-based networks 𝙘𝙖𝙣'𝙩 support ANY of these distributed AI scenarios - modern, open, cloud-native infrastructure 𝙘𝙖𝙣 support workloads wherever they need to run. Which means the pressing strategic question isn't "where will AI run?" It's: 𝗜𝘀 𝘆𝗼𝘂𝗿 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗔𝗜 𝘁𝗼 𝗿𝘂𝗻 𝗮𝗻𝘆𝘄𝗵𝗲𝗿𝗲? Over the next couple of weeks, I'll be exploring what "infrastructure readiness" actually means—and why the business case for network modernization just became overwhelming. But first: How are you thinking about infrastructure readiness for distributed AI workloads? #TelecomTransformation #EdgeAI #NetworkModernization #InfrastructureStrategy

Why Edge AI Data Centers Are Becoming a Game-Changer.... . . As Saudi Arabia accelerates its digital transformation under Vision 2030, a new wave of infrastructure is emerging: Edge AI Data Centers—localized, high-performance compute hubs designed to process data and run AI models closer to users, industries, and connected devices. Unlike traditional centralized cloud setups, edge-based AI cuts latency, boosts security, and enables real-time decision-making across sectors like smart cities, autonomous transport, and industrial automation. (Reference: Gcore Press Release – Ezditek AI Factory) Why Edge Matters More in Saudi Arabia? The Kingdom’s scale and rapidly expanding AI ecosystem make edge computing essential. Real-time analytics from IoT sensors, large camera networks, and industrial operations require local processing rather than relying on distant cloud regions. Beyond speed, this enhances data sovereignty, compliance, and supports localized AI for Arabic-focused models. (Reference: edgeIR – Saudi Arabia AI Infrastructure Report) Leading Players Shaping the Edge AI Landscape A major driver is ezditek, partnering with Gcore to build nine data centers in Riyadh, Jeddah, and Dammam to handle GPU-based AI workloads. Their “AI Factory” initiative delivers full-stack AI infrastructure, from training to deployment, within Saudi borders. (Reference: Ezditek + Gcore Partnership Announcement) Global Tech Partnerships Fueling AI at the Edge Saudi Arabia is expanding edge capabilities through global alliances. HUMAIN, the PIF-backed AI company, signed with Qualcomm to develop AI chips, edge infrastructure, and next-gen data centers optimized for high-density inference workloads, positioning the Kingdom as a hub for applied AI, not just cloud consumption. (Reference: DatacenterDynamics – Qualcomm & Humain Deal) Local Innovation: Saudi-Built Edge Platforms Local firms are also building native solutions. Edarat Group launched Edarat Edge, a full-stack edge AI platform offering predictive insights, secure analytics, and real-time processing across remote industrial environments and smart city layers, ensuring compliance and agility. (Reference: Edarat Group – Edge Platform Overview) What This Means for the Kingdom’s Digital Future Edge AI Data Centers will redefine how data is processed and monetized locally. Businesses get faster AI inference and more data control, startups gain access to local compute capacity, and government ecosystems enable critical infrastructure like transportation and energy automation. Early adopters will gain the competitive edge. (Reference: Saudi Vision 2030 Digital Economy Pillars) Your Turn: Let’s Discuss Which sector do you think will benefit most from Saudi Arabia’s Edge AI shift, smart cities, industrial automation, cybersecurity, healthcare, or another field?