AI &amp; Machine Learning

Cloud vs. Edge: Striking the Perfect Computing Balance for Your Business

AI & Machine Learning

Myth-Busting: Edge Computing Is Only for Replacing Cloud-Based Analytics Methods

Jonny Cameron 14 May 2025

Replacing cloud analytics with edge is a tempting idea. Edge systems are fast, local, and can run independently of a network. So naturally, some assume that once you have edge capabilities, there’s no more need for cloud-based analytics.

But that’s not how modern analytics works.

Edge computing doesn’t replace the cloud. And it definitely doesn’t replace your cloud analytics stack. Instead, it fills a gap that cloud analytics alone can’t cover—bringing real-time insight to the edge, while continuing to feed the cloud with data for deeper, longer-term analysis.

Why the confusion?

Cloud analytics have been the gold standard for years. You collect data, send it to the cloud, and let your analytics platform turn it into charts, dashboards, and decisions. That model still works, especially when you’re looking at historical data or organization-wide trends.

Edge computing sounds like a different world. Suddenly, data is being analyzed on a factory floor, inside a delivery vehicle, or in a retail store. No dashboards. No round trips to the cloud. Just instant feedback from the device itself.

That shift can feel like a replacement. But in reality, it’s a layer, not a swap.

What edge analytics actually means

Edge analytics is about time and location. Instead of sending raw data off for processing, edge devices analyse it right there, on-site, in real time.

Examples:

A vibration sensor detects a shift in a machine and flags it before failure occurs
A smart shelf tracks product movement and updates local stock counts instantly
A building management system adjusts HVAC settings based on occupancy and temperature data – without waiting on a cloud signal

These insights don’t need to go to the cloud first. They’re local decisions made from local data, right when it matters.

But here’s the catch: they’re still analytics, and more often than not, that data still finds its way into your broader analytics workflows.

The cloud still plays a critical role

Cloud analytics isn’t going anywhere. You still need it for:

Aggregating data from multiple edge locations
Visualising trends across time
Building dashboards for leadership and operations
Running advanced models for forecasting, inventory planning, and more

Edge analytics improves what cloud-based tools can’t always do: act quickly, close to the data source. But it also improves the cloud by filtering and enriching the data before it ever arrives at your central platform.

This means fewer duplicates, cleaner inputs, and more context-aware insights.

Edge and cloud analytics work better together

Here’s how a hybrid analytics workflow might look:

A device captures data and runs local ML inference to detect an event
That event is logged immediately, and action is taken on-site
Periodic summaries are sent to the cloud to populate dashboards or feed other systems
The cloud aggregates this across hundreds of sites for business-wide analysis

With this setup, you’re not duplicating your analytics stack—you’re extending it. Edge becomes the front line for immediate action, and the cloud remains your core for strategy and scale.

Where Simply NUC fits into edge analytics

Simply NUC systems are purpose-built for this kind of hybrid approach.

Our rugged devices, like the extremeEDGE Servers™, can crunch real-time sensor data and feed alerts into local control systems. Meanwhile, more compact models like Cyber Canyon NUC 15 Pro are ideal for retail or smart office environments where light analytics and cloud syncing go hand in hand.

We have systems that support AI inference, run lightweight analytics frameworks locally, and integrate easily with cloud dashboards and platforms.

And with NANO-BMC, you can remotely manage analytics endpoints without sending teams on-site.

You don’t need to rebuild your analytics stack – you just need to extend it smartly.

It’s not a takeover, it’s a team-up

Edge computing isn’t coming for your cloud analytics platform. It’s going to make it better.

By pushing quick, local decisions to the edge and letting the cloud handle long-term insight and coordination, you get the best of both worlds – faster reactions, smarter strategies, and cleaner, more relevant data at every level.

If you’re using cloud analytics today, great. You’re already halfway there. The next step? Let edge take some of the pressure off and help your insights move faster.

Useful Resources

Edge computing technology
Edge server
Edge computing for retail

Edge computing technology
Edge server
Edge computing for retail

AI & Machine Learning

Myth-Busting: Edge Machine Learning Runs on Powerful, Expensive Hardware

Jonny Cameron 13 May 2025

For years, AI was a resource-hungry technology, associated with massive infrastructure and elite-level hardware. But that thinking doesn’t reflect where edge ML is today.

The truth? You don’t need oversized gear or oversized budgets to run ML at the edge. You just need the right-sized hardware and a clear idea of what your workload actually requires.

Let’s break it down.

Where this myth came from

Machine learning started as a heavy lift. Training large models involved big datasets, serious compute power, and racks of high-performance servers. It made sense that many people associated AI with large-scale setups.

Then edge computing solutions entered the picture. Suddenly, AI was being deployed to remote sites, factory floors, and mobile devices. With that came a common misunderstanding: that you still needed the same level of horsepower, just in a smaller box.

What many teams overlook is the difference between training and inference.

Inference is lighter than you think

Most edge machine learning use cases don’t involve training models from scratch. They focus on inference, which means running a trained model to make decisions or predictions in real time.

This type of processing is far less demanding. Thanks to tools like TensorFlow Lite, ONNX Runtime, and PyTorch Mobile, even complex models can be slimmed down, optimized, and deployed to compact edge devices.

Techniques like quantization and model distillation help reduce model size and improve speed. This makes it possible to run AI tasks on low-power systems without heavy resource demands.

Edge-ready hardware doesn’t need to be overbuilt

Simply NUC’s range of edge-ready devices shows how ML can run efficiently on smaller, more affordable systems.

In commercial or controlled environments, we give you flexibility.

Take the Cyber Canyon NUC 15 Pro. It’s small, quiet, and powerful enough for edge ML tasks like predictive maintenance, in-store foot traffic analysis, or camera-based analytics. With up to Intel Core i7 processors and high-speed DDR5 memory, it delivers reliable performance in a compact footprint.

And if you’re building out a highly scalable deployment where cost, size, and modularity matter, Simply NUC’s Mini PC lineup – including models like Topaz and Moonstone – offers efficient, compact systems ready for AI inference at scale.

Many of these devices also support AI accelerators such as Intel Movidius or NVIDIA Jetson modules. That means you can run hardware-accelerated inference without needing a traditional GPU.

What can you actually run?

Here are just a few edge ML applications that run smoothly on compact, cost-effective Simply NUC devices:

Smart surveillance using AI to detect motion, intrusions, or identify faces
Retail insights from video analytics tracking customer behavior
Predictive maintenance based on sensor readings in manufacturing equipment
License plate recognition for smart parking or gated access
Building automation through occupancy-aware lighting and HVAC control

None of these require a full-scale server or expensive compute stack. You just need the right model, the right tools, and hardware that fits the job.

It’s not about power. It’s about fit.

The biggest shift in edge ML isn’t the hardware itself. It’s the mindset. Instead of asking, “What’s the most powerful device we can afford?”, a better question is, “What’s the most efficient way to run this task?”

Overbuilding hardware wastes energy, drives up costs, and creates more maintenance overhead. That’s not smart infrastructure. That’s just excess.

Simply NUC helps you avoid that trap. Our systems are configurable, scalable, and designed to give you just enough performance for what your use case needs – without overcomplicating your setup.

You can start small and scale smart

Edge machine learning doesn’t need to be complicated or expensive. With today’s tools, lightweight frameworks, and fit-for-purpose hardware, most teams can get started faster and more affordably than they might expect.

Whether you're deploying a single prototype or rolling out across multiple retail locations, there’s no need to overdo it. Choose the right model, deploy it locally, and scale as you grow.

Need help finding the right fit?
Simply NUC offers a full range of edge ML-capable systems – from rugged to commercial, from entry-level to AI-accelerated. If you’re not sure what you need, let’s talk. We’ll help you match your ML workload to the system that makes the most sense for your environment, your budget, and your goals.

Useful Resources

Edge computing technology
Edge server
Edge computing for retail

AI & Machine Learning

Myth-Busting: Edge Computing Means the End of the Cloud

Jonny Cameron 9 May 2025

If you've been keeping up with tech trends, you might have encountered the bold claim that edge computing is set to replace the cloud.

It’s an exciting headline, but it’s far from the truth. Sure, edge computing is growing rapidly, and it’s a game-changer in many scenarios. But the idea that it signals the death of the cloud? That’s a myth we’re here to bust.

The reality? Edge and cloud are not rivals. They’re teammates, each playing a specific role in modern IT infrastructures. Get ready as we set the record straight.

If you want to find out more about how edge can support your existing cloud infrastructure. Read our free ebook here.

Why the myth exists

Edge computing solutions have been gaining a lot of attention, with headlines about AI on the edge, real-time analytics, and decentralized processing. And for good reason. Moving data processing closer to where it’s created reduces latency, saves bandwidth costs, and enables faster decision-making.

But as "edge" becomes the buzzword of the moment, some folks have begun to think that edge computing is meant to replace the cloud entirely.

What edge computing really does

Here’s what edge computing is actually about. Imagine sensors on a factory floor, a self-driving car, or a smart display in a retail store. All of them generate data in real-time, and decisions need to be made on the spot. That’s where edge computing works wonders.

By processing data locally, edge solutions reduce the delay (or latency) that happens when information has to make a round trip to a faraway cloud data center. It’s faster, more private, and cuts bandwidth costs. Edge also excels in environments with unreliable connectivity, allowing devices to operate autonomously and upload data later when it’s practical.

Essentially, edge computing is perfect for localized, real-time workloads. But that doesn’t mean the cloud is out of the picture.

Why the cloud still matters

The cloud isn’t going anywhere, and here’s why: The cloud offers unmatched scalability, storage capacity, and centralization. It’s the powerhouse behind global dashboards, machine learning model training, and long-term data storage.

For example, while edge devices might process data locally for immediate decisions, that data often flows back to the cloud for deeper analysis, coordination, and storage. Think predictive models being retrained in the cloud based on fresh, edge-generated data. Or a global retail chain using cloud insights to fine-tune inventory management across multiple locations.

Bottom line? Cloud computing handles the heavy lifting that edge setups can’t. Together, they’re stronger than either one alone.

The real strategy is hybrid

The future of IT infrastructure isn’t a choice of edge or cloud. It’s the smart integration of both. Edge and cloud working together is the ultimate power move.

Here are a few real-world examples of hybrid systems in action:

Edge AI, cloud brains: Real-time decisions like defect detection on a manufacturing line happen locally at the edge. But insights from those detections sync with the cloud for retraining AI models.
On-site monitoring, global oversight: Edge devices monitor systems in remote locations, while the cloud provides a centralized dashboard for company-wide visibility.
Batching for bandwidth: IoT devices collect data offline in areas with poor connectivity, then upload it in bulk to the cloud when a stable connection is available.

Simply put, hybrid setups are about using the right tool for the right job.

How Simply NUC bridges the gap

At Simply NUC, we’re bridging the edge and cloud like never before. Our extremeEDGE Servers™ are built to thrive in localized environments while staying seamlessly connected to the cloud.

Here’s how Simply NUC makes edge-to-cloud integration effortless:

Cloud-ready out of the box: Whether you’re using AWS, Azure, or Google Cloud, Simply NUC edge systems sync with major cloud platforms while remaining fully capable of operating autonomously.
Flexible modular architecture: Our compact systems can be deployed where data is generated, from factory floors to trucks, scaling your edge workforce without overbuilding.
AI-ready hardware: Integrated GPUs and hardware acceleration options mean tasks like vision processing or predictive analytics run efficiently at the edge. Results can then be synced with the cloud for storage or further analysis.
Reliable, rugged systems: Shock-resistant, temperature-tolerant, and fanless designs ensure our products thrive in challenging environments while staying connected to centralized cloud systems.

Whether you need local processing, cloud syncing, or a mix of both, Simply NUC is here to make your edge-cloud strategy as seamless and scalable as possible.

It’s not either/or—but both

Don’t believe the myth that edge will make the cloud obsolete. The truth is that edge computing complements cloud technology, and the smartest IT strategies use both in tandem.

Want to see how edge and cloud can work together in your business? Explore Simply NUC’s edge-ready solutions to discover how we bring speed and flexibility to your infrastructure without sacrificing the power of the cloud.

Useful Resources

Edge computing technology
Edge server
Edge computing for retail

AI & Machine Learning

Myth-Busting: Edge AI Is Too Complex to Set Up

Jonny Cameron

Just when you get used to the idea of AI, along comes “Edge AI”.

But that’s the myth. And it’s time we cleared it up.

The truth? Edge AI has come a long way in a short space of time and setting it up is more approachable than most people think.

Why this myth exists in the first place

Because “AI” and “edge computing” are both complex topics on their own, combining them sounds like it would double the effort. Spoiler: it doesn’t anymore.

Edge AI setup isn’t what it used to be (in a good way)

Today, it’s a different world. The tools have matured, the hardware has gotten smarter, and the whole process is a lot more plug-and-play than people expect.

Here’s what’s changed:

Hardware is ready to roll
Devices like Simply NUC’s extremeEDGE Servers™ come rugged, compact, and purpose-built to handle edge workloads out of the box. No data center needed.
Software got lighter and easier
Frameworks like TensorFlow Lite, ONNX, and NVIDIA’s Jetson platform mean you can take pre-trained models and deploy them without rewriting everything from scratch.
You can start small
Want to run object detection on a camera feed? Or do real-time monitoring on a piece of equipment? You don’t need a full AI team or six months of setup. You just need the right tools, and a clear use case.

Real-world examples that don’t require a PhD

Edge AI is already working behind the scenes in more places than you might expect. Here’s what simple deployment looks like:

A warehouse installs AI-powered cameras to count inventory in real time.
A retail store uses computer vision to track product placement and foot traffic.
A hospital runs anomaly detection locally to spot equipment faults early.
A transit hub uses license plate recognition—on-site, with no cloud lag.

All of these can be deployed on compact systems using pre-trained models and off-the-shelf hardware. No data center. No endless configuration.

The support is there, too

Here’s the other part that makes this easier: you don’t have to do it alone.

We’ve helped teams deploy edge AI in manufacturing, healthcare, logistics, retail, you name it. We’ve seen firsthand how small, agile setups can make a huge difference.

Edge AI doesn’t have to be hard

So here’s the bottom line: Edge AI isn’t just for tech giants or AI labs anymore. It’s for real-world businesses solving real problems – faster, smarter, and closer to where the data lives.

Yes, it’s powerful. But that doesn’t mean it has to be complicated.

If you’re curious about how edge AI could fit into your setup, we’re happy to show you. No jargon, no overwhelm, just clear steps and the right-sized solution for the job.

Useful Resources

Edge computing technology
Edge server
Edge computing in smart cities

AI & Machine Learning

Myth-Busting: AI Hardware Is a One-Size-Fits-All Approach

Jonny Cameron 6 May 2025

What happens when a business tries to use the same hardware setup for every AI task, whether training massive models or running real-time edge inference? Best case, they waste power, space or budget. Worst case, their AI systems fall short when it matters most.

The idea that one piece of hardware can handle every AI workload sounds convenient, but it’s not how AI actually works.

Tasks vary, environments differ, and trying to squeeze everything into one setup leads to inefficiency, rising costs and underwhelming results.

Let’s unpack why AI isn’t a one-size-fits-all operation and how choosing the right hardware setup makes all the difference.

Not all AI workloads are created equal

Some AI tasks are huge and complex. Others are small, fast, and nimble. Understanding the difference is the first step in building the right infrastructure.

Training models

Training large-scale models, like foundation models or LLMs takes serious computing power. These workloads usually run in the cloud on high-end GPU rigs with heavy-duty cooling and power demands.

Inference in production

But once a model is trained, the hardware requirements change. Real-time inference, like spotting defects on a factory line or answering a voice command, doesn’t need brute force, it needs fast, efficient responses.

A real-world contrast

Picture this: you train a voice model using cloud-based servers stacked with GPUs. But to actually use it in a handheld device in a warehouse? You’ll need something compact, responsive and rugged enough for the real world.

The takeaway: different jobs need different tools. Trying to treat every AI task the same is like using a sledgehammer when you need a screwdriver.

Hardware needs change with location and environment

It’s not just about what the task is. Where your AI runs matters too.

Rugged conditions

Some setups, like in warehouses, factories or oil rigs—need hardware that can handle dust, heat, vibration, and more. These aren’t places where standard hardware thrives.

Latency and connectivity

Use cases like autonomous systems or real-time video monitoring can’t afford to wait on cloud roundtrips. They need low-latency, on-site processing that doesn’t depend on a stable connection.

Cost in context

Cloud works well when you need scale or flexibility. But for consistent workloads that need fast, local processing, deploying hardware at the edge may be the smarter, more affordable option over time.

Bottom line: the environment shapes the solution.

Find out more about the benefits of an edge server.

Right-sizing your AI setup with flexible systems

What really unlocks AI performance? Flexibility. Matching your hardware to the workload and environment means you’re not wasting energy, overpaying, or underperforming.

Modular systems for edge deployment

Simply NUC’s extremeEDGE Servers™ are a great example. Built for tough, space-constrained environments, they pack real power into a compact, rugged form factor, ideal for edge AI.

Customizable and compact

Whether you’re running lightweight, rule-based models or deep-learning systems, hardware can be configured to fit. Some models don’t need a GPU at all, especially if you’ve used techniques like quantization or distillation to optimize them.

With modular systems, you can scale up or down, depending on the job. No waste, no overkill.

The real value of flexibility

Better performance

When hardware is chosen to match the task, jobs get done faster and more efficiently, on the edge or in the cloud.

Smarter cloud / edge balance

Use the cloud for what it’s good at (scalability), and the edge for what it does best (low-latency, local processing). No more over-relying on one setup to do it all.

Smart businesses are thinking about how edge computing can work with the cloud. Read our free ebook here for more.

Scalable for the future

The right-sized approach grows with your needs. As your AI strategy evolves, your infrastructure keeps up, without starting from scratch.

A tailored approach beats a one-size-fits-all

AI is moving fast. Workloads are diverse, use cases are everywhere, and environments can be unpredictable. The one-size-fits-all mindset just doesn’t cut it anymore.

By investing in smart, configurable hardware designed for specific tasks, businesses unlock better AI performance, more efficient operations, and real-world results that scale.

Curious what fit-for-purpose AI hardware could look like for your setup? Talk to the Simply NUC team or check out our edge AI solutions to find your ideal match.

Useful Resources

Edge computing platform
Fraud detection machine learning

Edge computing in agriculture

AI & Machine Learning

Myth-Busting: AI Applications Always Require Expensive GPUs

Jonny Cameron

One of the most common myths surrounding AI applications is that they require a big investment in top-of-the-line GPUs.

It’s easy to see where this myth comes from.

The hype around training powerful AI models like GPT or DALL·E often focuses on high-end GPUs like NVIDIA A100 or H100 that dominate data centers with their parallel processing capabilities. But here’s the thing, not all AI tasks need that level of compute power.

So let’s debunk the myth that AI requires expensive GPUs for every stage and type of use case. From lightweight models to edge-based applications, there are many ways businesses can implement AI without breaking the bank. Along the way, we’ll show you alternatives that give you the power you need, without the cost.

Training AI models vs everyday AI use

We won’t sugarcoat it: training large-scale AI models is GPU-intensive.

Tasks like fine-tuning language models or training neural networks for image generation require specialized GPUs designed for high-performance workloads. These GPUs are great at parallel processing, breaking down complex computations into smaller, manageable chunks and processing them simultaneously. But there’s an important distinction to make here.

Training is just one part of the AI lifecycle. Once a model is trained, its day-to-day use shifts towards inference. This is the stage where an AI model applies its pre-trained knowledge to perform tasks, like classifying an image or recommending a product on an e-commerce platform. Here’s the good news—for inference and deployment, AI is much less demanding.

Inference and deployment don’t need powerhouse GPUs

Unlike training, inference tasks don’t need the raw compute power of the most expensive GPUs. Most AI workloads that businesses use, like chatbots, fraud detection algorithms or image recognition applications are inference-driven. These tasks can be optimized to run on more modest hardware thanks to techniques like:

Quantization: Reducing the precision of the numbers used in a model’s calculations, cutting down processing requirements without affecting accuracy much.
Pruning: Removing unnecessary weights from a model that don’t contribute much to its predictions.
Distillation: Training smaller, more efficient models to replicate the behavior of larger ones.By doing so, you can deploy AI applications on regular CPUs or entry-level GPUs.

Why you need Edge AI

Edge AI is where computers process AI workloads locally, not in the cloud.

Many AI use cases today are moving to the edge, using compact and powerful local systems to run inference tasks in real-time. This eliminates the need for constant back-and-forth with a central data center, resulting in faster response times and reduced bandwidth usage.

Whether it’s a smart camera in a retail store detecting shoplifting, a robotic arm in a manufacturing plant checking for defects or IoT devices predicting equipment failures, edge AI is becoming essential. And the best part is, edge devices don’t need the latest NVIDIA H100 to get the job done. Compact systems like Simply NUC’s extremeEDGE Servers™ are designed to run lightweight AI tasks while delivering consistent, reliable results in real-world applications.

Cloud, hybrid solutions and renting power

Still worried about scenarios that require more compute power occasionally? Cloud solutions and hybrid approaches offer flexible, cost-effective alternatives.

Cloud AI allows businesses to rent GPU or TPU capacity from platforms like AWS, Google Cloud or Azure, access top-tier hardware without owning it outright.
Hybrid models use both edge and cloud. For example, AI-powered cameras might process basic recognition locally and send more complex data to the cloud for further analysis.
Shared Access to GPU resources means smaller businesses can afford bursts of high-performance computing power for tasks like model training, without committing to full-time hardware investments.

These options further prove that businesses don’t have to buy expensive GPUs to implement AI. Smarter resource management and integration with cloud ecosystems can be the sweet spot.

To find out how your business can strike the perfect balance between Cloud and Edge computing, read our ebook.

Beyond GPUs

Another way to reduce reliance on expensive GPUs is to look at alternative hardware. Here are some options:

TPUs (Tensor Processing Units), originally developed by Google, are custom-designed for machine learning workloads.
ASICs (Application-Specific Integrated Circuits) take on specific AI workloads, energy-efficient alternatives to general-purpose GPUs.
Modern CPUs are making huge progress in supporting AI workloads, especially with optimisations through machine learning frameworks like TensorFlow Lite and ONNX.Many compact devices, including Simply NUC’s AI-ready computing solutions, support these alternatives to run diverse, scalable AI workloads across industries.

Simply NUC’s role in right-sizing AI

You don’t have to break the bank or source equipment from the latest data centre to adopt AI. It’s all about right-sizing the solution to the task. With scalable, compact systems designed to run real-world AI use cases, Simply NUC takes the complexity out of AI deployment.

Summary:

GPUs like NVIDIA H100 may be needed for training massive models but are overkill for most inference and deployment tasks.
Edge AI lets organisations process AI workloads locally using cost-effective, compact systems.
Businesses can choose cloud, hybrid or alternative hardware to avoid investing in high-end GPUs.
Simply NUC designs performance-driven edge systems like the extremeEDGE Servers™, bringing accessible, reliable AI to real-world applications.

The myth that all AI requires expensive GPUs is just that—a myth. With the right approach and tools, AI can be deployed efficiently, affordably and effectively. Ready to take the next step in your AI deployment?

See how Simply NUC’s solutions can change your edge and AI computing game. Get in touch.

Useful resources

Edge server

Edge computing for beginners

Edge computing in simple words

Computing on the edge

Edge computing platform

Edge devices

AI & Machine Learning

Myth-Busting: AI Is All About Data, Not the Hardware

Jonny Cameron

AI runs on data. The more data you feed into a system, the smarter and more accurate it becomes. The more you help AI learn from good data, the more it can help you. Right?

Mostly, yes. But there’s an often-overlooked piece of the puzzle that businesses can’t afford to ignore. Hardware.

Too often, hardware is seen as just the background player in AI’s success story, handling all the heavy lifting while the data algorithms get the spotlight. The truth, however, is far more nuanced. When it comes to deploying AI at the edge, having the right-sized, high-performance hardware makes all the difference. Without it, even the most advanced algorithms and abundant datasets can hit a wall.

It’s time to bust this myth.

The myth vs. reality of data-driven AI

The myth

AI success is all about having massive datasets and cutting-edge algorithms. Data is king, and hardware is just a passive medium that quietly processes what’s needed.

The reality

While data and intelligent models are critical, they can only go so far without hardware that’s purpose-built to meet the unique demands of AI operations. At the edge, where AI processing occurs close to where data is generated, hardware becomes a key enabler. Without it, your AI’s potential could be bottlenecked by latency, overheating, or scalability constraints.

In short, AI isn’t just about having the right “what” (data and models)—it’s about using the right “where” (scalable, efficient hardware).

Why hardware matters (especially at the edge)

Edge AI environments are very different from traditional data centers. While a data center has a controlled setup with robust cooling and power backups, edge environments present challenges such as extreme temperatures, intermittent power and limited physical space. Hardware in these settings isn’t just nice to have; it’s mission-critical.

Here’s why:

1. Real-time performance

At the edge, decisions need to be made in real time. Consider a retail store’s smart shelf monitoring system or a factory’s defect detection system. Latency caused by sending data to the cloud and back can mean unhappy customers or costly production delays. Hardware optimized for AI inferencing at the edge processes data on-site, minimizing latency and ensuring split-second efficiency.

2. Rugged and reliable design

Edge environments can be tough. Think factory floors, outdoor kiosks or roadside installations. Standard servers can quickly overheat or malfunction in these conditions. Rugged, durable hardware designed for edge AI is built to withstand extreme conditions, ensuring reliability no matter where it’s deployed.

3. Reduced bandwidth and costs

Sending massive amounts of data to the cloud isn’t just slow; it’s expensive. Companies can save significant costs by processing data on-site with edge hardware, dramatically reducing bandwidth usage and reliance on external servers.

4. Scalability

From a single retail store to an enterprise-wide deployment across hundreds of locations, hardware must scale easily without adding layers of complexity. Scalability is key to achieving a successful edge AI rollout, both for growing with your needs and for maintaining efficiency as demands increase.

5. Remote manageability

Managing edge devices across different locations can be a challenge for IT teams. Hardware with built-in tools like NANO-BMC (lightweight Baseboard Management Controller) lets teams remotely update, monitor and troubleshoot devices—even when they’re offline. This minimizes downtime and keeps operations running smoothly.

When hardware goes wrong

Underestimating the importance of hardware for edge AI can lead to real-world challenges, including:

Performance bottlenecks

When hardware isn’t built for AI inferencing, real-time applications like predictive maintenance or video analytics run into slowdowns, rendering them ineffective.

High costs

Over-reliance on cloud processing drives up data transfer costs significantly. Poor planning here can haunt your stack in the long term.

Environmental failures

Deploying standard servers in harsh industrial setups? Expect overheating issues, unexpected failures, and costly replacements.

Scalability hurdles

Lacking modular, scalable hardware means stalling your ability to expand efficiently. It’s like trying to upgrade a car mid-race.

Maintenance troubles

Hardware that doesn’t support remote management causes delays when troubleshooting issues, especially in distributed environments.All these reasons why hardware matters for edge AI.

What does it look like?

Edge AI needs hardware that matches the brain with brawn. Enter Simply NUC’s extremeEDGE Servers™. These purpose-built devices are designed for edge AI environments, with real-world durability and cutting-edge features.

Here’s what they have:

Compact, scalable

Extreme performance doesn’t have to mean big. extremeEDGE Servers™ scale from single-site to enterprise-wide in retail, logistics and other industries.

AI acceleration

Every unit has AI acceleration through M.2 or PCIe expansion for real-time inference tasks like computer vision and predictive analytics.

NANO-BMC for remote management

Simplify IT with full remote control features to update, power cycle and monitor even when devices are off.

Rugged, fanless

For tough environments, fanless models are designed to withstand high temperatures and space-constrained setups like outdoor kiosks or factory floors.

Real-world flexibility

Intel or AMD processors, up to 96GB RAM and dual LAN ports, extremeEDGE Servers™ meet the varied demands of edge AI applications.

Cost-effective right-sizing

Why spend data center-grade hardware for edge tasks? extremeEDGE Servers™ let you right-size your infrastructure and save costs.

Real world examples of right-sized hardware

The impact of smart hardware is seen in real edge AI use cases:

Retail

A grocery store updates digital signage instantly based on real-time inventory levels with edge servers, delivering dynamic pricing and promotions to customers.

Manufacturing

A factory detects vibration patterns in machinery using edge AI to identify potential failures before they happen. With rugged servers on-site, they don’t send raw machine data to the cloud, reducing latency and costs.

Healthcare

Hospitals use edge devices for real-time analysis of diagnostic imaging to speed up decision making without sending sensitive data off-site.

These examples show why you need to think beyond data. Reliable, purpose-built hardware is what turns AI theory into practice.

Stop Thinking “All Data, No Hardware”AI is great, no question. But thinking big data and sophisticated algorithms without hardware is like building a sports car with no engine. At the edge, where speed, performance and durability matter, a scalable hardware architecture like extremeEDGE Servers™ is the foundation for success.

Time to think beyond data. Choose hardware that matches AI’s power, meets real-world needs and grows with your business.

Learn more

Find out how Simply NUC can power your edge AI. Learn about our extremeEDGE Servers™

Useful resources

Edge server

Edge computing for beginners

Edge computing in simple words

Computing on the edge

Edge computing platform

Edge devices

AI & Machine Learning

5 Leading Edge Computing Platforms For 2025

Jonny Cameron 1 May 2025

Edge computing technology should be on the radar of any business that wants to move faster, smarter, and closer to the data that drives them.

Why? Because edge computing enables businesses to process data where it’s created. That reduces transmission costs, improves network bandwidth, and supports real-time data processing in places the cloud alone can’t reach. Whether it’s remote devices in the field or smart devices in a retail store, edge computing systems help teams perform faster, more secure operations, right at the source.

In this post, we’ll break down five edge platforms leading the charge in 2025. You’ll see how they help businesses analyze data, gather insight, and maintain control, from the edge to the cloud and back again.

Simply NUC: Custom edge computing devices built for the real world

If you need high-performance edge computing solutions that fit in the palm of your hand, Simply NUC delivers.

Simply NUC offers a full range of edge computing devices designed for fast, efficient data processing at the edge, where every second and every square inch matters. These systems come pre-configured or custom-built to support operational analytics, predictive maintenance, and AI at the edge.

Need rugged edge servers that can operate in harsh physical locations like factory floors or outdoor facilities? Simply NUC has you covered. Deploying into more commercial spaces like healthcare, retail, or education? Try the Cyber Canyon NUC 15 Pro, it is compact, quiet, and ready for workloads like patient data processing, smart security, and local automation.

Their systems support secure data collection, edge AI frameworks, and hybrid deployments that connect seamlessly with your cloud infrastructure. With support for edge security, remote management, and energy-efficient operating systems, Simply NUC is the go-to for businesses that need edge tech that just works.

The first of its kind, NANO-BMC out-of-band management in a small form factor enables remote management of edge devices. Find out more about extremeEDGE Servers™.

Amazon Web Services (AWS): Cloud meets edge at scale

AWS brings its powerful cloud computing platform to the edge with a suite of services designed for scalability and control.

Using AWS IoT Greengrass and edge-specific services, businesses can collect data and run edge computing software in real time. These tools connect directly with AWS’s massive cloud resources, allowing you to keep your edge operations local while syncing summaries or insights to the cloud.

Security is baked in, with advanced security controls and encryption protecting critical data across remote locations. Whether you're managing IoT devices in smart buildings or tracking logistics in the field, AWS provides a flexible bridge between the edge and the cloud.

Microsoft Azure IoT Edge: Smart edge with seamless integration

The Azure IoT Edge platform is Microsoft’s answer to distributed, intelligent edge computing.

With this system, businesses can gather data insights, deploy AI models, and run edge computing software directly on edge hardware. It integrates cleanly with the Microsoft Azure Admin Center, making it easy to manage devices, monitor performance, and scale quickly.

Edge security? Covered. The platform protects sensitive data, making it a solid choice for industries like healthcare or finance where compliance and privacy matter. And because it’s built on a hybrid cloud model, Azure lets you operate locally while staying connected to your centralized platform in the cloud.

Google Distributed Cloud: AI, edge analytics, and observability

The Google Distributed Cloud Suite and Google Distributed Cloud Edge offerings bring Google’s AI and cloud tools closer to where data originates.

You can run workloads on edge infrastructure, including remote devices and local clusters, using an integrated development environment that supports containerized apps and ML models. Whether you're doing predictive maintenance, tracking environmental conditions, or enabling fog computing in a manufacturing setting, Google helps you do it right at the edge.

Security is a major focus. Google supports integration with third party security services to reduce security risks and improve edge observability. For teams that already rely on Google Cloud, this is a natural step forward.

HPE GreenLake: Flexible edge for complex networks

HPE GreenLake is a strong choice for businesses that need edge connectivity products across distributed networks or industrial sites.

This edge computing service operates on a pay-per-use hybrid cloud model, which means you only pay for what you use, and can scale your edge access as your business grows. It’s particularly effective for complex setups like private cloud environments or real-time analytics in energy and logistics.

GreenLake gives you tools to manage data collected across multiple edge locations, along with robust security controls and built-in tools to analyze data close to the source. It’s also optimized for remote visibility, so you stay in control no matter where your infrastructure lives.

Why edge computing matters now more than ever

If you’ve been waiting for the right moment to adopt edge computing, 2025 is it.

Today’s edge platforms are no longer niche solutions. They’re robust, reliable, and designed to work with the cloud infrastructure and analytics tools you already use. More than ever, edge computing enables businesses to improve operational efficiency, reduce reliance on centralized cloud systems, and make smarter decisions in real time.

Whether you’re focused on reducing network bandwidth usage, managing smart devices, or making the most of data insights across multiple sites, the edge has become an essential part of modern infrastructure.

Want to bring edge computing closer to your data?

Simply NUC offers compact, configurable systems built for real-world edge challenges. Let’s talk about how we can help you extend your cloud computing strategy – without losing speed, control, or visibility at the edge.

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What is an edge server used for?

Jonny Cameron 28 April 2025

Imagine asking a smart assistant like Alexa to turn off the lights, but instead of responding instantly, it takes a full minute to process your request. Or think of a video stream that constantly buffers because it has to send all that data to a distant server for processing before delivering it back to your device.

Seconds matter. Consumers and businesses are demanding faster, localized solutions to handle data processing.

This is where edge computing comes in. And a key part of the edge computing ecosystem is the edge server.

An edge server acts like a local branch office for data processing. Instead of sending information to a distant data center or relying entirely on cloud computing, an edge server processes data locally, close to where it’s generated. This improves response times, reduces transmission costs and ensures low latency (reducing delays) for critical tasks.

What is an edge server?

This is a specialized type of server located at the network edge, close to the end devices or systems generating data. Unlike traditional servers, which are centralized and often located in massive data centers, edge servers process and analyze data at its source.

Think of an edge server as a fast, local assistant. It performs tasks like processing data locally, filtering unnecessary information, and sending only the most important results to the central cloud computing system. This makes everything faster and more efficient, especially for applications that rely on real-time data processing.

Your smart watch is a good example. Data processing happens directly on the device rather than relying on distant cloud servers and constant connectivity. This means that sleep patterns and heart rate can give you instance feedback.

How does an edge server work?

Data is generated at the edge: Devices like smart cameras, IoT sensors, or even autonomous vehicles collect data in real-time.
Data is processed locally: Instead of sending all that data to a traditional data center, an on-premise edge server or edge compute platform processes it nearby.
Insights are sent to the cloud: After processing data locally, only relevant insights or summaries are sent to the cloud for storage or deeper analysis.

This distributed nature of edge computing helps reduce latency, improve data security, and increase efficiency by cutting down on unnecessary data transmission.

How is it different from traditional servers?

The biggest difference lies in location and purpose:

Traditional servers are centralized, handling large-scale tasks in data centers far from the user.
Edge servers are decentralized, designed to work closer to the physical location where data is generated, such as an IoT sensor or on-premises edge system.

Edge servers often use specialized hardware like field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs) to handle specific tasks efficiently. Their compute resources are tailored to the needs of edge workloads, from managing smart cities to enabling predictive maintenance in industrial settings.

Extreme environments

Simply NUC’s extremeEDGE servers™ have a rugged design that is built to last in extreme environments. Think up a mountain, down a hole or in a very hot warehouse or kitchen.

NANO-BMC technology allows IT teams to efficiently monitor, update, and remotely manage servers, even when devices are powered off.

Key benefits of edge servers

Improved response times

One of the key advantages of edge computing is its speed. Specifically, the ability to process data where it’s generated rather than sending it off to a distant data center. That local handling means much lower latency, which is vital for any application that depends on quick decision-making.

Take smart cities, for example. Edge servers help traffic systems respond in real time , adjusting lights based on congestion, rerouting traffic flows during emergencies and keeping intersections running smoothly without waiting on cloud instructions.

In retail, it’s about keeping up with the customer… literally. Edge servers allow stores to update digital signage, pricing, and inventory systems instantly. So when a flash sale kicks in or a product goes out of stock, the system adjusts on the spot, without a delay. Even checkout queues move faster when edge devices are handling point-of-sale data in real time, rather than relying on a slow connection to HQ.

The result? Whether you're managing traffic on a busy street or syncing shelves in a high-footfall shop, edge computing enables fast, responsive experiences that traditional setups just can’t match.

These systems are also resilient to drops in network connectivity, which makes them ideal for environments like smart cities or transport hubs. In a traffic management scenario, for example, the ability to perform real-time monitoring at each edge location helps cities respond faster to changing road conditions.

Enhanced efficiency

Edge servers ease the burden on centralized cloud systems by handling a significant portion of the data locally. This reduces the volume of data that needs to travel across networks, saving bandwidth and cutting transmission costs.

For example:

IoT devices in industrial automation can send only critical alerts to the cloud while processing routine data on the edge server, increasing overall efficiency.
Content delivery networks (CDNs) use edge servers to cache frequently accessed data close to users, reducing load times and improving performance for streaming and other online services.

This localized approach makes edge servers a cost-effective solution for industries managing large-scale data generation.

Real-time decision-making when it counts

Some systems can’t afford a delay, not even a second. Whether it’s a piece of machinery about to overheat or a patient’s heart rate dropping suddenly, waiting on cloud processing just isn’t an option.

In healthcare, for instance, wearable devices powered by edge servers can track a patient’s vitals in real time and alert staff to anything unusual immediately. No lag. No waiting for a data packet to bounce through a data center.

And in the world of autonomous vehicles, it’s all about reacting on the spot. Cars rely on edge processing to make split-second decisions based on sensor and camera data. Everything from braking to obstacle avoidance happens locally, right at the edge. If that decision had to travel to the cloud and back, it would already be too late.

That’s why edge servers are becoming essential in any scenario where reaction time is non-negotiable.

Keeping data close and secure

There’s also the question of trust. Sensitive data, like medical records, production stats, or customer details, shouldn’t have to travel miles to be processed. Edge servers let businesses handle that data where it’s created, reducing the risk that comes with sending it across networks.

Picture a factory floor. Instead of pushing production metrics to a central server, an edge server can process it on-site, flag anomalies, and adjust in real time, without opening the door to external threats.

In healthcare, it’s about more than just speed. Local edge processing supports compliance with strict data regulations by keeping patient information close to home and under tighter control.

Since businesses can tailor the security settings on their own edge deployments, they gain flexibility. There’s no one-size-fits-all model, just the right protections for the job.

Edge computing doesn’t just improve performance. It gives you more control over the things that matter most: privacy, protection, and peace of mind.

What’s happening right now with edge computing

It’s not edge vs cloud anymore

Let’s be honest, most businesses don’t care whether the data runs through edge nodes or the cloud, they just want it to be fast and reliable. What’s actually happening out there is a bit of both.

Say you’ve got an online store. You need the checkout process to feel instant, especially during sales. Edge hardware steps in to handle that locally. Price updates, stock counts, even the offers that pop up when you browse, those can all be powered on-site. Meanwhile, the cloud’s doing the long-term number crunching in the background.

And then there’s the stuff you don’t notice, like streaming. When a website or video loads fast, chances are it’s because edge servers already have that content cached nearby. No need to wait for it to come from the other side of the world.

So, it’s not really an either-or. It's more like a tag team. The edge handles the now, the cloud handles the rest.

Read our free 39 page ebook edge vs. cloud

IoT is pushing edge to the front

There’s just too much data being generated for the cloud to handle all of it. Every connected device; smart cameras, sensors, machines are feeding information back constantly. That’s where edge servers come in.

Think of a voice assistant in your home. When you ask something simple, you don’t want it to lag. The quicker it responds, the better it feels. That speed usually comes from processing the request close by, not from bouncing it off a server overseas.

Or take a factory floor. Machines are monitored in real time. Something starts vibrating in the wrong way? The edge server catches it before it becomes a problem. No need to ship that data off to the cloud and wait.

This kind of on-the-spot processing isn’t flashy, but it’s what keeps things running. Especially when the network connection isn’t great or when timing really matters.

AI and machine learning at the edge

Edge servers aren’t just built for durability anymore – they’re getting smarter, too. Many now include extra processing hardware like FPGAs or ASICs, which means they can handle machine learning tasks right there on-site. No need to wait on the cloud. It’s a shift toward AI edge computing, where local data is processed immediately, thanks to purpose-built processing capabilities that eliminate delays.

This kind of setup gives businesses more control and faster results in the real world. For example:

A camera on a production line can detect defects in real time using AI running locally on an edge node. There’s no delay, and the data never has to leave the site.
AR headsets in the field can respond instantly by processing data at the edge, no lag, no dropped frames, just a seamless experience.

When systems don’t rely so heavily on central servers, things just move faster. More importantly, they work when and where they need to. For businesses, that means smarter services delivered closer to the user, with less waiting, fewer costs, and fewer points of failure.

How enterprise teams are putting edge servers to work in 2025 and beyond

Edge computing isn’t theory anymore, it's rolling out across sectors, solving practical problems in all kinds of environments.

Edge computing in manufacturing involves edge servers supporting predictive maintenance, tracking asset performance and helping production teams optimize workflows as conditions change all without pushing every bit of data back to the cloud.

In retail, proximity matters. With edge hardware closer to stores or distribution centres, retailers can respond in the moment updating digital signage, adjusting pricing, or tracking footfall trends as they happen.

Find out more about edge computing for retail.

Entertainment platforms are also getting a boost. By streaming from edge servers placed closer to viewers, they can reduce buffering and improve quality without overloading a central server farm.

Behind the scenes, these systems often run with support from specialised hardware and more flexible software setups that allow teams to adjust or scale based on the needs of each location.

Some businesses are even taking things a step further with fog computing, building a more connected layer between edge and cloud. It’s a flexible model, one that makes sense when you need the speed of local processing, but still want to tap into the scale of the cloud when required.

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