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Edge computing in financial services

Understanding edge computing and sustainability

As technology continues to scale, so does its environmental footprint. The good news is, using edge computing solutions offers a more efficient way to handle data, one that’s better for business and better for the planet.

Instead of sending information across long distances to centralized cloud servers, edge computing processes data closer to where it’s created. This localized approach doesn’t just speed things up; it also reduces energy use and helps lower the environmental impact of digital operations.

Why edge computing supports greener tech

Sustainability in tech is about rethinking how systems are designed. By moving away from massive, energy-intensive data centers, edge computing helps organizations meet sustainability targets while improving performance.

Here are a few ways edge computing supports more responsible infrastructure:

Improved energy efficiency
Local data processing cuts down on the energy needed to transmit information across long networks.
Less reliance on large data centers
With more tasks handled at the edge, there’s less pressure on central servers that consume huge amounts of power.
Lower latency, higher efficiency
When systems respond faster, they work smarter. That means less energy wasted in waiting, rerouting, or reprocessing.
Better use of IoT resources
Devices that process data locally can make smarter decisions in real time, which helps reduce overall energy use.

These shifts might seem small in isolation, but together, they can significantly reduce the carbon footprint of everyday digital activity.

The environmental cost of centralized cloud models

Traditional cloud infrastructure leans heavily on centralized data centers. These facilities require a lot of power to run and even more to cool. That setup creates several challenges:

High power usage
Data centers demand large-scale electricity just to stay online, even when handling routine tasks.
Excessive heat output
Servers generate heat that must be constantly managed through cooling systems, which adds to total energy consumption.
Increased carbon emissions
Sending data long distances over global networks burns energy and contributes to higher carbon output.

When you compare this to edge computing, where processing happens closer to the user, it’s easy to see the sustainability benefits. Fewer trips to the cloud means less energy spent and more efficient use of hardware on the ground.

A more efficient path forward

Edge computing technology is helping organizations process data where it’s generated, instead of relying on centralized servers or distant cloud data centers.

This approach reduces energy usage, shortens the path for data transmission, and allows for faster response times.

By deploying edge devices across local networks, businesses can cut down on unnecessary cloud traffic, reduce electricity consumption, and ease the load on data storage systems. The shift toward real time data processing doesn’t just improve network speed or operational efficiency; it also supports sustainability strategies by reducing power consumption and limiting reliance on more data centers.

Whether it's powering smart buildings, enabling responsive IoT networks, or streamlining enterprise data workflows, edge computing solutions are helping businesses move toward more sustainable practices without sacrificing performance.

Use cases that show the sustainability benefits of edge computing

Edge computing's role in sustainability goes well beyond speed. It’s playing a part in how industries rethink infrastructure, minimizing greenhouse gas emissions, reducing electronic waste, and improving resource efficiency.

Let’s look at how edge servers are enabling businesses across different sectors to reduce energy consumption and move toward a more sustainable future.

Smarter energy management in modern grids

Real time data processing is key to maintaining balance and reliability. Edge computing devices installed across the grid allow for instant monitoring and adjustments based on demand.

Data from sensors is processed at the edge, which reduces the need to send all this data to cloud computing platforms. As a result, these systems require less computing power, reduce power consumption, and optimize how energy flows from renewable energy sources like solar and wind.

The outcome is clear: less energy waste, improved electricity distribution, and more efficient operations.

Supporting sustainable cities

Urban environments generate massive amounts of data, traffic flows, public transport schedules, air quality readings, and more. Edge computing stores and processes that data locally, making it easier for systems to respond in real time.

Edge-powered platforms support smart city applications like AI-powered traffic signals and dynamic waste collection routes. By handling data closer to the source, cities reduce network traffic, improve decision-making, and reduce their reliance on centralized cloud data storage. That translates into reduced energy requirements and better support for long-term sustainability strategies.

Energy-efficient smart homes and smart buildings

IoT-enabled devices are everywhere, from thermostats and lighting systems to smart plugs and HVAC units. With edge computing, these electronic devices don’t have to rely on cloud data centers for every function. Instead, they make localized decisions using built-in computing power.

This shift results in lower data transmission needs and meaningful energy savings for consumers.

It also helps manufacturers position energy-efficient edge computing devices as part of a greener technology stack, appealing to homeowners who want to reduce energy usage and support more sustainable operations.

Lower-impact healthcare systems

Healthcare is generating more data than ever. From remote patient monitoring to advanced machine learning diagnostics, real time analysis is critical, but relying on cloud computing alone adds strain to centralized infrastructure.

Edge computing allows wearable medical devices and monitoring tools to process patient data on-site. This helps reduce reliance on backend systems, minimizes electricity consumption, and lowers the environmental impact tied to powering and cooling cloud infrastructure.

Telemedicine systems benefit too. Edge computing keeps services online and responsive without relying solely on large-scale data centers, improving both efficiency and sustainability across the healthcare technology stack.

Challenges and solutions in building sustainable edge systems

While edge computing has clear benefits for sustainability, it isn’t without its hurdles. Like any shift in technology infrastructure, the transition to a more energy-efficient model comes with trade-offs that need thoughtful planning.

Here’s a closer look at the common challenges and how organizations are working through them.

Challenges to consider

Initial energy demand

Rolling out edge devices at scale often increases total hardware usage. That means energy consumption can rise at the beginning of a deployment, even if it lowers over time.

Renewable integration isn’t automatic

Bringing clean energy into edge infrastructure isn’t always straightforward. Powering local systems with renewables depends on access, geography, and planning, and those pieces don’t always align out of the box.

Harder-to-monitor infrastructure

Edge systems are spread out, which makes it more difficult to track and optimize energy performance. Without proper tools, maintaining sustainable practices across multiple locations can be a challenge.

Practical solutions that make a difference

Low-power edge devices

Choosing energy-efficient hardware helps reduce the impact of large-scale rollouts. Smaller devices with optimized power usage can provide the performance needed without unnecessary draw.

Smarter management platforms

Monitoring platforms designed for distributed systems can give teams real-time visibility into energy use, performance, and uptime. This kind of insight helps ensure systems run as efficiently as possible.

Working with renewable energy providers

Partnering with green energy suppliers, or building edge infrastructure near renewable sources, can help ensure systems run on clean power. It’s an extra step that adds long-term value for both sustainability and resilience.

By facing these challenges head-on and applying the right tools, organizations can keep their sustainability efforts on track while still taking advantage of the performance benefits edge computing provides.

How edge computing helps reduce carbon over time

The long-term sustainability of edge computing lies in its ability to do more with less; less distance, less energy, and less reliance on centralized infrastructure. By processing data where it’s created, edge systems reduce the need to push everything back to the cloud. That leads to more efficient energy use and a lower overall footprint.

As businesses explore more decentralized energy models and adopt green initiatives, edge computing fits naturally into the strategy. Here’s how:

Smaller, cleaner energy footprints
Local systems make it easier to run on solar, wind, or other renewable sources, reducing dependency on traditional grids.
More sustainable digital infrastructure
With less pressure on data centers and a shift to smarter local processing, edge computing makes it easier for businesses to operate sustainably.
Support for global emission goals
By reducing redundant cloud traffic and unnecessary energy use, edge computing plays a role in helping industries lower their carbon output.

Looking ahead, the environmental impact of digital systems will only become more important. Edge computing gives businesses the tools to build for performance today, while helping protect the environment for tomorrow.

*Sustainability survey

AI & Machine Learning

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

Jonny Cameron 15 May 2025

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

At first it conjures images of servers in remote locations, machine learning models, industrial systems, and maybe even a few sci-fi undertones. It sounds like something that requires a team of engineers and a mountain of infrastructure just to get started.

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

A few years ago, getting AI to run at the edge wasn’t easy. You had to pull together custom-built hardware, optimize machine learning models by hand, and write scripts just to get devices talking to each other. It worked, but only for the teams with deep technical know-how and plenty of resources.

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.

When you work with a partner like Simply NUC, you get more than just a box. You get hardware tuned to your use case, documentation to walk you through setup, and support when you need it. You can even manage devices remotely using side-band management, so once your systems are up and running, they stay that way.

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 for retail

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 for beginners

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 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™

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Edge Computing in Healthcare

Jonny Cameron 30 April 2025

The healthcare industry generates a huge amount of patient data every day, from electronic health records and diagnostic scans to wearable monitors and telemedicine interactions. Handling all this data efficiently isn't just important; it directly affects the quality of patient care and outcomes. That's where edge computing comes into play, offering an innovative approach by processing data right where it's created – whether that's in a hospital, a local clinic, or even at a patient's home.

Unlike traditional cloud computing, which sends data to distant centralized servers, edge computing processes information locally. This reduces delays, ensures faster data handling for critical applications, and enhances security by limiting the amount of sensitive patient information traveling over networks. For healthcare, where even a few seconds can make a huge difference, edge computing means quicker decision-making, tighter data security, and new ways to deliver patient care.

How edge computing transforms healthcare

Edge computing supports healthcare across diverse environments—from busy urban hospitals to remote rural clinics—by bringing powerful data-processing capabilities closer to the action. This localized processing leads to faster, safer, and more efficient management of medical information and patient care.

Remote patient monitoring

Wearable devices are becoming central to healthcare, monitoring vital signs like heart rate, blood pressure, and oxygen saturation continuously. Edge devices process this data in real time, so medical professionals can instantly react if something unusual happens.

For instance: A patient with diabetes or heart conditions wears a monitoring device that immediately alerts healthcare providers to any anomalies.

Impact: Proactive chronic disease management reduces hospital visits and helps catch health issues early.

Telemedicine and low-latency diagnostics

Telemedicine requires instant data processing for successful remote consultations. With edge computing, clinics in remote areas can smoothly deliver high-quality video consultations, share medical images, and instantly access patient histories—even when internet connections aren't robust.

For example: A rural health center leverages edge computing for seamless video consultations with specialists in distant cities.

Impact: Faster, more accessible healthcare even in underserved areas, enhancing patient outcomes.

Medical imaging and diagnostics

Medical imaging equipment, like MRI or CT scanners, can now process high-quality images directly at the location they're captured. Edge computing allows instant analysis of these images, significantly reducing wait times for results.

Example: An MRI machine processes imaging data right after scans, enabling doctors to make quicker, more accurate diagnoses.

Impact: Improved patient outcomes through quicker, more accurate diagnostic capabilities.

Emergency response systems

Ambulances equipped with edge computing devices can securely share vital patient data in real time with hospitals during transportation, providing emergency teams crucial information even before the patient arrives.

Example: Paramedics use edge-enabled monitors to transmit vital signs to hospital emergency teams ahead of arrival.

Impact: Better-prepared emergency rooms, faster treatments, and improved patient survival rates.

Understanding "edge" in healthcare

In healthcare, the "edge" is simply the point where data is initially generated and processed—like hospitals, ambulances, clinics, or patient homes. Processing data at these locations offers quicker response times, improved security, and better use of healthcare resources.

Healthcare edge devices

Edge devices in healthcare handle real-time data processing right at the source, enhancing both patient care and hospital efficiency. Common examples include:

Wearables: Monitor health metrics like heart rhythms or blood sugar, instantly alerting doctors to irregularities.
IoT sensors: Continuously monitor patients in critical care settings, offering live updates to medical staff.
Diagnostic imaging tools: Perform local analysis of medical scans for quicker diagnostics.

Integration with existing healthcare infrastructure

Edge computing integrates smoothly into current healthcare setups, improving data management and operational efficiency:

Electronic Health Records (EHR): Real-time updates to patient records without compromising security.
Clinical decision systems: Immediate insights help doctors make quick, informed decisions during surgeries or critical interventions.

Edge computing in rural healthcare

Edge computing is especially powerful in rural areas, helping clinics efficiently manage patient care despite limited network connectivity.

Example: Rural clinics process diagnostic results locally and easily share insights with specialists in bigger cities for deeper analysis.

Practical examples of edge computing in healthcare

Edge computing is already making a huge impact in healthcare with applications like:

Real-time patient monitoring

Wearable devices continuously analyze patient health metrics, alerting medical staff immediately if issues arise.

Example: A wearable cardiac device detects irregular heart rhythms and instantly notifies a doctor.

Impact: Enhanced management of chronic conditions and reduced hospitalization rates.

AI-powered diagnostics

AI applications running on edge computing platforms provide faster, more accurate diagnostic insights directly at healthcare facilities.

Example: A hospital uses edge-based AI tools to rapidly analyze CT scans, accelerating diagnosis.

Impact: Quicker disease detection and treatment.

Remote surgical assistance

Advanced edge solutions enable remote surgical guidance, allowing specialists to assist in operations from afar using robotic systems and augmented reality.

Example: A surgeon in an urban hospital guides procedures at a rural clinic remotely.

Impact: Increased access to specialized care and precision during critical surgeries.

Telemedicine platforms

Edge computing ensures smooth telemedicine experiences by supporting real-time communication and rapid access to patient records.

Example: Virtual consultations become seamless and reliable, even in areas with unstable internet.

Impact: Wider access to healthcare, particularly for remote and underserved communities.

Edge-enabled ambulances

Real-time patient monitoring and data sharing in ambulances allow hospitals to prepare better for incoming emergencies.

Example: Ambulance teams send live updates on patient vitals to ER staff.

Impact: More efficient emergency responses and improved survival rates.

The role of edge servers in healthcare

Edge servers store and process medical data locally at healthcare facilities, significantly improving response times and data security.

Real-time analysis and security

Edge servers handle intensive tasks like analyzing medical images or monitoring patient data in real-time, significantly reducing response delays.

Example: Edge servers in hospitals process CT scans instantly for radiologists.

Impact: Faster diagnostics, enhanced patient outcomes, and improved data privacy by keeping patient information onsite.

Scalability and flexibility

Edge servers easily adapt to new technologies, supporting evolving healthcare requirements like AI-powered diagnostics, telemedicine, and IoT-enabled patient monitoring.

Example: A hospital expands its edge infrastructure to include AI tools for rare disease diagnosis.

Impact: Greater service capabilities and readiness for future innovations.

Edge computing is shaping the future of healthcare by providing quicker, safer, and more reliable solutions—helping providers deliver the exceptional care their patients deserve.

Useful Resources

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Powering the Future: Edge Computing in Smart Cities

Jonny Cameron 29 April 2025

edge computing in smart cities traffic lights

Edge computing has transformative potential in urban environments by processing data closer to the source, reducing latency and enabling instant decision making. Unlike the traditional cloud centric model, edge computing decentralizes data processing, using local nodes, micro data centers and edge devices embedded in city infrastructure to process data in real time.

This is critical in smart cities where a growing network of IoT sensors and devices demands fast local computation to ensure systems like transportation and utilities can respond to rapid changes in the environment.

Smart cities are using edge computing to make urban living better through various applications. By embedding edge devices in city infrastructure, cities can process massive data locally and have responsive urban systems.

For example, intelligent traffic management systems use edge computing to analyze traffic congestion data in real time and adjust traffic signal timings to optimize flow and reduce delays. This not only improves commuter safety but also reduces emissions by minimizing idle times.

Furthermore, edge computing supports energy optimization in smart grids. By monitoring energy consumption patterns in real time, edge devices enable smart grids to adjust power distribution in real time and integrate renewable energy sources seamlessly.

This reduces energy waste and supports sustainable urban development.

Urban infrastructure applications

Edge computing solutions are key to public safety in smart city environments. Video surveillance systems with edge analytics can detect and respond to incidents in real time. For example, edge enabled security cameras can process video feeds locally to detect unusual activities and trigger alerts to authorities without sending large video data to central servers. This reduces bandwidth congestion and ensures timely responses.

These applications show how edge computing creates ecosystems that prioritize speed, adaptability and efficiency to improve urban life. By embedding edge computing in various smart city applications, cities can create an urban digital network that supports dynamic structures and connected systems.

For more examples of edge computing, check out our guide to edge computing examples.

Technological advancements in edge computing

One of the biggest advancements is the integration of 5G networks. With ultra low latency and high bandwidth, 5G accelerates data transfer between edge devices, enabling real time urban applications like autonomous vehicles and emergency response systems. This ensures data generated by various smart city applications is processed fast and effectively. The combination of edge computing and artificial intelligence (AI) has enabled smarter systems to do real time analytics and autonomous decision making. AI driven processing at the edge can recognize patterns in traffic flows or energy usage and make predictive adjustments without relying on central computation. This optimizes energy usage and supports smart city operations that are more responsive and efficient.

Another key development is the edge-to-cloud continuum which allows data sharing and analysis between edge nodes and central cloud servers.

This balances the immediacy of edge processing with the computational power of cloud analysis for long term decision making and short term needs.

By using edge computing infrastructure cities can have increased reliability, connectivity and user centric design.

For businesses looking to implement edge computing solutions understanding these technological advancements is key.

Find out more about edge computing for small business.

Challenges and solutions in edge computing

While edge computing has huge potential for smart cities, its implementation is not without challenges. One of the biggest is data security and privacy. Decentralizing data introduces vulnerabilities at multiple endpoints and requires robust encryption, multi layered authentication and continuous monitoring to secure edge systems and protect sensitive information. This is critical to maintain data integrity processed by edge devices in smart city infrastructure.

Scalability is another big challenge. Expanding edge computing infrastructure to support dense urban populations requires scalable solutions. Lightweight, modular deployments like micro data centers and portable edge nodes offer flexible and cost effective scalability. These solutions allow smart city projects to grow and evolve without compromising performance or efficiency.

Integrating edge computing with existing urban frameworks can also be complex. Collaboration between technology providers and urban planners and adopting adaptable software solutions can simplify this process. By embedding edge computing in existing urban systems cities can move computational tasks closer to where data is generated and make smart city operations more responsive and efficient.

For those new to the concept check out our edge computing for beginners guide to navigate these challenges and implement effective edge computing solutions.

Edge computing in smart cities future

The future of edge computing in smart cities is exciting with innovations that will change urban living. One of the expected developments is smarter autonomy. By combining edge computing with advanced AI urban systems such as vehicles, utilities and public safety responses will become more autonomous and adapt to their environment. This will make smart city connectivity more efficient and responsive and urban life more seamless and integrated.

Sustainability

Sustainability is another area where edge computing will make a big impact. Real time energy optimization powered by edge analytics will support green urban initiatives, reduce resource waste and optimize renewable energy integration. This will contribute to the development of green cities that prioritize sustainability and environmental responsibility.

Citizen participation is also on the horizon. Smart city applications enabled by edge computing may allow residents to interact more with urban services. For example mobile apps could allow citizens to report issues directly to local processing systems and create a more engaged and responsive urban community.

These developments will shape cities that are not just intelligent but also sustainable, responsive and inclusive. For more on how edge computing is transforming various sectors check out our IoT and edge computing insights.

Edge for a smarter future

As cities evolve the integration of edge computing into smart city infrastructure will be a key driver of urban innovation. By using edge technology cities can enhance their urban systems and create environments that are not only more efficient but also more adaptable to the needs of their citizens. The decentralized data processing of edge computing allows for real time data processing and analysis and smart city operations to remain responsive and effective.

Edge trends show a shift towards more local and immediate data handling which is essential for managing the massive data generated by modern urban life. This shift will support the development of urban digital networks that prioritize both technology and human centric design.

For businesses and city planners looking to stay ahead of the curve understanding and implementing edge computing solutions will be key. By embracing these solutions cities can become smarter, more sustainable and more connected and improve urban life for all.

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Edge Computing for Retail: Smarter Stores, Better Experiences

Jonny Cameron 25 April 2025

Cars driving passed on both sides of a busy road

Do you ever turn into a monster as soon as you walk into a shop? Expecting everything to run smoothly and not have to wait for anything?

Whether walking into a physical store or placing an online order, you want accurate stock, quick service, and a smooth experience every time.

Edge computing for retail is helping stores deliver on those expectations – by putting powerful computing capabilities right where the action happens: in-store.

From real-time inventory monitoring to analyzing customer movement through the aisles, edge technology is changing what’s possible in the retail sector – and doing it without the need for constant reliance on cloud data centers or fragile internet connections.

Here’s how edge computing is creating better retail experiences, reducing operational costs, and enabling a new era of responsive, data-driven shopping.

The challenge: legacy systems, slow data, and rising expectations

Modern shoppers want more visibility, more personalization, and fewer hiccups. But many stores are still relying on legacy systems built for yesterday’s demands. That makes it hard to track what’s on the store shelves, adapt to changing consumer preferences, or avoid the dreaded "out of stock" sign.

Meanwhile, customer expectations keep climbing. They want accurate stock information in-store and online. They want personalized offers. They want checkout to be fast – and ideally, self-service.

This is where edge computing enables real change.

Why edge computing for retail matters now

Edge computing puts processing power at the edge locations – on-site, in the store – rather than at a distant centralized location. That means stores can:

Respond to customer interactions in real time
Monitor inventory and customer flow without delays
Process sensor data locally, even if internet access drops
Protect sensitive customer data by keeping it in-store

This localized power means retailers can spot empty shelves instantly, re-route products more efficiently, and give retail workers access to up-to-date info – all without relying solely on a centralized cloud system.

Real-time retail: what it looks like

Edge computing isn’t just a buzzword. It’s powering practical tools that are already improving retail operations. Here are just a few ways it’s being used:

1. Real-time inventory monitoring

Edge devices track products as they move – from the warehouse to the back room to the shelf. When paired with cameras or shelf sensors, edge computing solutions help identify stockouts and prevent lost sales.

It’s the difference between learning about an empty shelf after a customer walks out – or before they ever notice it.

2. Customer insights without the creepiness

Using computer vision and in-store sensors, retail applications can now observe customer movement, identify high-traffic zones, and track how shoppers interact with displays. Crucially, this data is processed locally, protecting sensitive data while still offering valuable insights into store layout and product engagement.

Retailers can then adjust signage, promotions, or product placement – all based on what’s actually happening on the floor.

3. Self-checkout with real-time validation

Self-checkout machines rely on real-time data processing to recognize items, verify payment, and prevent errors. When the system can analyze data on-site, transactions move faster – and errors are resolved more quickly.

The same cameras and edge computing devices can also help flag issues like theft or abandoned baskets, improving security and maintaining business continuity.

Beyond the store: connecting across multiple locations

For retailers operating in multiple stores, edge computing offers a more scalable infrastructure than traditional systems. Rather than routing every bit of data through a centralized location, edge solutions help enable businesses to manage retail infrastructure locally, while syncing with a broader cloud computing platform when needed.

This hybrid model offers the best of both worlds: quick, responsive store operations on-site, and broader visibility across regions.

Bringing AI to the edge

AI is no longer just for labs or data centers. In retail, artificial intelligence is powering smarter decisions at the store level – helping with inventory management, staffing forecasts, and even personalized promotions.

Running AI models at the edge means stores can offer this intelligence without delay or dependency on cloud speed. It’s fast, it’s secure, and it’s adaptable.

Improving customer loyalty through smarter data use

Great experiences build customer loyalty. And that starts with customer data that’s accurate, protected, and actionable.

By keeping data generation and data analysis close to where it happens – on the store floor – edge computing for retail allows stores to respond in the moment. Whether that’s recommending products, flagging purchase patterns, or just making sure the product they came in for is actually on the shelf.

With real-time data analysis, customer satisfaction improves, and so does your bottom line.

Why Simply NUC?

At Simply NUC, we design edge computing solutions that work in real-world retail environments. Our small-form-factor devices pack serious power – supporting everything from inventory management and computer vision applications to cloud connectivity and on-site AI.

Whether you're building new retail infrastructure or looking to upgrade existing systems, our devices help retail organizations reduce operational costs, improve responsiveness, and gain the flexibility to grow.

Final thought: smarter retail starts at the edge

As the retail industry continues to evolve, continuous innovation is key to staying competitive. Edge computing gives stores the tools to react faster, serve better, and adapt to what modern shoppers really want.

Ready to bring smarter experiences to your retail store?

Talk to Simply NUC about edge computing for retail – and see how your store can work smarter from the shelf to the cloud.

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