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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

Fraud detection in banking

Edge computing for small business

Edge computing in healthcare

Edge computing in manufacturing

Let’s talk about something that holds a lot of businesses back from diving into edge machine learning.

It’s this idea that building and deploying ML at the edge is only for the elite-the Fortune 500s with deep R&D budgets and teams of machine learning engineers in lab coats.

Here’s the good news: that’s a myth. And we’re here to bust it.

Edge ML isn’t just for the big players anymore. Thanks to better tools, lighter frameworks, and right-sized hardware, getting started is more doable than ever. You don’t need a million-dollar budget to make it work-you, just need the right setup.

Why this myth stuck around

Let’s be fair. A few years ago, this wasn’t entirely wrong.

Machine learning was notoriously compute-heavy. Training models meant huge datasets, long processing times, and some serious GPU firepower. Add the challenge of deploying those models on devices out in the wild, and yeah-it sounded like a job for a Silicon Valley startup, not a mid-sized operations team.

The learning curve was real. And so was the cost.

But things have changed.

The reality: it’s getting easier-fast

Today, you don’t have to train models from scratch or design every component yourself. Most of what businesses need for edge ML already exists.

Pre-trained models are everywhere-whether you’re detecting objects, recognizing faces, spotting equipment faults, or reading license plates. And thanks to frameworks like TensorFlow Lite, ONNX, and PyTorch Mobile, these models can be compressed, optimized, and deployed on small edge devices without needing a room full of servers.

Techniques like quantization (which shrinks model size) and model distillation (which simplifies complex models for smaller devices) help get your AI up and running where it matters-without crushing your power budget or blowing past your memory limits.

The hardware is already here-and affordable

The idea that edge ML requires specialized, ultra-expensive hardware? That’s outdated too.

Take Simply NUC’s extremeEDGE Servers™. These are compact, rugged systems designed specifically for edge environments-places like warehouses, factory lines, retail counters, and transport hubs.

They’re modular, configurable, and come with options to include (or skip) discrete GPUs, depending on what your workload needs. They also support hardware accelerators like Intel Movidius or NVIDIA Jetson, which deliver big performance in a small footprint.

Unlike traditional servers, they don’t need a climate-controlled room and a full-time sysadmin to keep them running. They just work-right where you need them.

Real-world examples that prove the point

You don’t need to look far to see how approachable edge ML has become.

Here are just a few things companies are already doing-with tools and systems that are off-the-shelf and budget-friendly:

• Retail: Counting foot traffic and tracking shelf engagement with AI-powered cameras
• Warehousing: Scanning inventory and recognizing packaging anomalies in real time
• Manufacturing: Detecting early signs of machine failure using vibration and temperature sensors
• Smart buildings: Using ML to control HVAC or lighting based on learned occupancy patterns
• Transport: Running local license plate recognition for access control and traffic monitoring

None of these required starting from scratch. Most used pre-trained models, lightweight frameworks, and rugged edge devices, like those from Simply NUC, to get started fast and scale as needed.

You don’t need to go it alone

Another reason people assume edge ML is hard? They think they’ll have to figure it all out themselves.

You don’t.

At Simply NUC, we work with businesses every day to configure the right system for their edge AI needs. Whether you’re starting with a simple proof of concept or rolling out across multiple locations, we’ve got your back.

Our systems are designed to play nicely with popular frameworks and cloud platforms. We provide documentation, guidance, and ongoing support. Our edge hardware includes NANO-BMC management, so you can remotely monitor, update, and troubleshoot your fleet-even when your devices are powered down.

You’re not alone in this. And you’re not expected to be an AI expert just to get started.

Edge ML is more accessible than you think

We get it, edge machine learning sounds complex. But the tools have come a long way. The hardware is ready. And the myth that it’s only for deep-pocketed, highly technical teams? That one’s officially retired.

What matters now is your use case. If you’ve got a real-world challenge-like reducing downtime, tracking activity, or improving on-site decision-making-chances are, edge ML can help. And it doesn’t have to break your budget or your brain to get started.

Let’s make edge ML doable

Thinking about what’s possible in your business? Let’s talk. Simply NUC builds edge-ready, AI-capable systems that take the pain out of deployment-so you can focus on results, not requirements.

Useful Resources

Edge computing for beginners

Edge computing in simple words

Computing on the edge

Edge computing platform 

Edge devices

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

Fraud detection in banking

Edge computing for small business

Edge computing in healthcare

Edge computing in manufacturing

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

Fraud detection in banking

Edge computing for small business

Edge computing in healthcare

Edge computing in manufacturing

When most people picture AI in action, they imagine endless racks of servers, blinking lights, and the hum of cooling systems in a remote data center. It’s a big, dramatic image. And yes, some AI workloads absolutely live there.

But the idea that every AI application needs that kind of infrastructure? That’s a myth, and it’s long overdue for a rethink.

In 2025, AI is showing up in smaller places, doing faster work, and running on devices that would’ve been unthinkable just a few years ago. Not every job needs the muscle of a hyperscale setup.

Let’s take a look at when AI really does need a data center (and when it doesn’t).

When AI needs a data center

Some AI tasks are just plain massive. Training a large language model like GPT-4? That takes heavy-duty hardware, enormous datasets, and enough processing power to make your electric meter spin.

In these cases, data centers are essential for:

• Training huge models with billions of parameters
• Handling millions of simultaneous user requests (like global search engines or recommendation systems)
• Analyzing petabytes of data for big enterprise use cases

For that kind of scale, centralizing the infrastructure makes total sense. But here’s the thing, not every AI project looks like this.

When AI doesn’t need a data center

Most AI use cases aren’t about training, they’re about running the model (what’s known as inference). And inference can happen in far smaller, far more efficient places.

Like where?

• On a voice assistant in your kitchen that answers without calling home to the cloud
• On a factory floor, where machines use AI to predict failures before they happen
• On a smartphone, running facial recognition offline in a split second

These don’t need racks of servers. They just need the right-sized hardware, and that’s where edge AI comes in.

Edge AI is changing the game

Edge AI means running your AI models locally, right where the data is created. That could be in a warehouse, a hospital, a delivery van, or even a vending machine. It’s fast, private, and doesn’t rely on constant cloud connectivity.

Why it’s catching on:

• Lower latency – Data doesn’t have to travel. Results happen instantly.
• Better privacy – No need to ship sensitive info offsite.
• Reduced costs – Less data in the cloud means fewer bandwidth bills.
• Higher reliability – It keeps working even when the internet doesn’t.

This approach is already making waves in industries like healthcare, logistics, and manufacturing. And Simply NUC’s compact, rugged edge systems are built exactly for these kinds of environments.

Smarter hardware, smaller footprint

The idea that powerful AI needs powerful real estate is outdated. Thanks to innovations in hardware, AI is going small and staying smart.

Devices like NVIDIA Jetson or Google Coral can now handle real-time inference on the edge. And with lightweight frameworks like TensorFlow Lite and ONNX, models can be optimized to run on compact systems without sacrificing performance.

Simply NUC’s modular systems fit right into this shift. You get performance where you need it without the weight or the wait of data center deployment.

The bottom line: match the tool to the task

Some AI jobs need big muscle. Others need speed, portability, or durability. What they don’t need is a one-size-fits-all setup.

So here’s the takeaway: Instead of asking “how big does my AI infrastructure need to be?” start asking “where does the work happen and what does it really need to run well?”

If your workload lives on the edge, your hardware should too.

Curious what that looks like for your business?
Let’s talk. Simply NUC has edge-ready systems that bring AI performance closer to where it matters fast, efficiently, and made to fit.

Useful Resources

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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 technology
Edge server
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Fraud detection machine learning

Edge computing in agriculture

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 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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You know what can make or break your AI workflows? Your tools. Even the most talented minds in AI hit roadblocks when their computing hardware can't keep up with the breakneck pace of innovation. That's where the NUC 15 Pro Cyber Canyon comes in. This compact computing powerhouse is designed to optimize every aspect of your AI work, wherever that work happens.

Whether you're running machine learning models, managing edge deployments, or fine-tuning AI solutions at your desk, the Cyber Canyon delivers seamless performance, advanced AI acceleration, and the flexibility to do it all.

Here's how the NUC 15 Pro Cyber Canyon can transform AI operations for you.

Where performance meets productivity

One of the standout features of the Cyber Canyon is its 99 TOPS of AI acceleration. That's thanks to the latest Intel® Core Ultra 2 processors. More specifically, the Arrow Lake H with advanced CPU cores, next-gen Intel® Arc GPU, and NPU, which combined elevate performance to new heights in the new AI-computing era. For AI developers, that means local inference, training data models, and deploying neural networks can happen fast, efficiently, and productively. You get to decide where your projects go from there, while reducing the need to rely on cloud resources.

Key Processor Features:

• Dedicated AI cores and Vision Processing Unit (VPU) with 35% faster inference performance vs the previous generation.
• Up to 24 cores (16 Efficiency + 8 Performance) with max clock speed ~5.8 GHz.
• Integrated Intel® Arc™ Graphics with Intel® Xe-LPG Gen 12.9, giving up to 64 execution units, supporting up to four 4K or one 8K display.

With up to DDR5-6400 memory and Gen4 NVMe storage, you’ll see reduced bottlenecks and faster model processing, which translates directly to better workflow efficiency.

Keep AI local, secure and efficient

While cloud-based AI has its strengths, there are growing cases where local processing offers unparalleled advantages. The NUC 15 Pro Cyber Canyon allows businesses and developers to keep sensitive data onsite, reducing latency, minimizing cloud costs, and maintaining strict data privacy.

For industries like healthcare, retail, or manufacturing, where security and speed are crucial, Cyber Canyon provides an edge that cloud computing simply can’t match.

Benefits of local AI processing:

• Lower Latency: Immediate responses without waiting for cloud processing
• Enhanced Privacy: Improved security by keeping sensitive data in-house
• Cost Efficiency: Cut down recurring cloud costs while maintaining quality performance

Cyber Canyon can include Intel® vPro® Technology, which ensures enhanced remote manageability and advanced threat detection. IT teams benefit from having a secure, reliable platform for running AI workloads without compromise.

Next-gen connectivity to plug into any workflow

AI workflows don’t exist in a bubble. Often, they require integration with a wider network of devices and processes. Fortunately, Cyber Canyon is built for multi-connectivity.

Future-proofed with the latest Wi-Fi 7 and Bluetooth 5.4, the NUC 15 Pro is built to be a reliable hub for high-speed, next-gen connectivity.

Features like dual Thunderbolt™ 4 ports, HDMI 2.1, abundant USB-A and USB-C I/O, and 2.5Gb Ethernet make Cyber Canyon a seamless fit within any advanced system. Whether you’re connecting external GPUs for tensor operations, processing data from sensors, or managing edge AI devices, this machine is built to handle it all.

It even supports quad 4K displays, making it the perfect device for real-time AI applications requiring visualization or dashboards.

And if your system needs to grow? Cyber Canyon’s tool-less 2.0 tall chassis design makes expansion effortless, providing slots for extra storage or PCIe add-ons.

Compact form, massive potential

Modern AI demands high-powered machines, but it doesn’t demand the bulk of traditional workstations. That’s where the compact design of Cyber Canyon stands out (but not literally, it’s small).

At just 0.48L for the Slim chassis or 0.7L for the Tall chassis, the NUC 15 Pro Cyber Canyon fits anywhere—from cluttered offices to isolated industry deployments. Its MIL-STD-810H certification ensures it can handle harsh environments too. Portable yet powerful, it’s the perfect workstation for labs, edge setups, and corporate offices alike.

And don’t be fooled by its small size. Its performance easily rivals that of full-size desktops, all while staying energy-efficient and whisper-quiet.

Real-World Applications of Cyber Canyon for AI

The NUC 15 Pro Cyber Canyon is engineered to meet the demands of professionals across various industries. Here’s how it excels in real-world scenarios:

1. AI Development and Training

Optimize development cycles with powerful local processing and quick adjustments to models.

2. Edge Computing

Deploy real-time AI inferencing at the edge for IoT applications or industry automation. Evaluate and respond to data instantly without cloud reliance.

3. Healthcare

Process sensitive patient data securely, allowing health facilities to employ AI in diagnostics and treatment recommendations while meeting strict privacy standards.

4. Retail

Provide dynamic, real-time pricing or personalized shopping experiences with instant response powered by on-site AI engines.

5. Media Production and Creative Workflows

For creators working with AI-enhanced video editing, rendering, or content generation, Cyber Canyon’s hardware boosts creativity without delays, ready with the latest Microsoft Copilot out of the box.

Why Cyber Canyon is built for the future of AI

Every component of Cyber Canyon is purpose-built for modern and future AI workflows. By blending high performance, security, and scalability into a form factor designed for versatility, it empowers businesses, developers, and enterprises to push the boundaries of innovation.

Whether you're fine-tuning an advanced marketing recommendation engine, testing ML models in a lab, or processing sensory input in a factory, Cyber Canyon brings you the ability to do more, faster, and smarter.

Let your AI workflows work better with Cyber Canyon

With the Simply NUC 15 Pro Cyber Canyon, you have a long-term ally designed to help you succeed.

Want to experience the benefits firsthand?

Explore how Cyber Canyon can redefine the way you approach AI.

Useful Resources

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Sound familiar?

You’re evaluating your hardware options and leaning towards off-the-shelf solutions. Maybe it seems like the safer, more budget-friendly choice. After all, custom hardware gets a reputation for being expensive, right? But what if that assumption isn’t entirely true? Could this be limiting your potential to achieve better performance and cost savings for your business?

Let’s take a look.

The myth of custom hardware costs

The idea that “custom hardware is too expensive” comes from a surface level comparison. Off-the-shelf solutions are built for mass production, often with a lower upfront cost. They appeal to businesses looking for quick and easy solutions. But these solutions often come with hidden costs and limitations that only become apparent after deployment.

Standard hardware is designed for the broadest possible audience, so it’s rarely optimized for your business needs. You may end up paying for features you don’t need or worse, compensating for underpowered capabilities with additional upgrades. That’s where custom hardware shines.

The hidden costs of off-the-shelf solutions

On the surface, off-the-shelf solutions may seem cost effective, but they come with trade-offs that businesses can’t ignore. Here’s what gets overlooked:

1. Paying for features you don’t need

Off-the-shelf solutions are designed for the widest possible range of users. What if your business doesn’t need top end graphics or excessive storage? With standard devices you’ll still pay for those features. Custom hardware lets you invest in only what you need.

2. Underperformance leading to inefficiencies

Has your team experienced slow response times or performance bottlenecks? Standard solutions prioritize broad appeal over specialized functionality so they’re not suited for specific workloads like data analytics, AI model training or industrial automation. This inefficiency can hurt productivity and lead to additional system upgrades or workarounds.

3. Shorter lifespan and higher upgrade costs

Standard solutions are built without future scalability in mind. This means shorter lifespans and businesses have to replace earlier. Custom hardware, tuned to your needs, is better equipped to handle changing demands and extend its lifespan and reduce long term costs.

4. Wasted power and higher operational expenses

Generic solutions have one-size-fits-all power configurations, so you waste energy. For power hungry IT environments this means higher operational costs. By specifying energy efficient components, custom hardware eliminates unnecessary power consumption.

Why custom hardware makes sense

Custom hardware lets businesses invest in optimized performance so every dollar spent contributes to specific goals. Here’s how it benefits you in the long run:

1. Pay for what you need, not for what you don’t

Imagine being able to configure your system with just the processing power, memory and storage you need for your specific workload. Custom hardware gives you that control, so you don’t pay for features or capabilities you don’t use.

2. Performance lowers operational costs

Purpose built hardware means smoother workflows. Highly optimized for specific tasks it minimizes downtime and maximizes efficiency so you save time and operational expenses.

3. Longer lifespan and scalability

Custom solutions aren’t just built for current needs; they’re designed for growth. Modularity and upgradability means your hardware can adapt as your business evolves, reducing the frequency of costly replacements.

4. Energy efficiency for cost savings

By selecting only the components you need for your operations, custom hardware can reduce energy consumption dramatically. This doesn’t just save you money on power bills; it also aligns with sustainability goals, a win-win for cost and corporate responsibility.

5. Simplified IT maintenance

Custom systems are easier to deploy and maintain because they’re built with your existing infrastructure in mind. This reduces the workload for IT departments, saving on labor costs and minimizing downtime.

Real world examples of cost effective custom hardware

To bring this to life here are a few use cases where custom hardware is the smarter financial choice:

AI and machine learning

A mid-sized retailer reduced cloud processing costs by deploying custom AI hardware for edge computing. The solution allowed them to process complex models locally, avoiding exorbitant cloud fees.

Retail and POS systems

A point-of-sale (POS) provider chose custom mini PCs for their terminals, saving on hardware requirements while ensuring operational reliability and compact design.

Healthcare imaging

A hospital upgraded diagnostic imaging equipment with custom configured systems for AI driven diagnostics. This resulted in faster results and cost savings by reducing power consumption.

Industrial automation

An engineering firm deployed ruggedized custom hardware for edge computing to prevent costly downtime in harsh industrial environments.

Simply NUC solutions for businesses looking for efficiency

If you’re considering custom hardware Simply NUC combines technical expertise with cost effective solutions. Our modular, customizable systems are built to your needs so you only pay for what you need.

Here’s what Simply NUC offers:

1. Customizable mini PCs: These systems can be configured with the processing power, memory and storage you need.
2. Scalable performance: Whether you need AI, data analytics or industrial capabilities Simply NUC has systems built for specific workloads.
3. Sustainable and cost efficient designs: Lower energy consumption and upgradable hardware reduces total cost of ownership (TCO).
4. Edge computing solutions: For businesses that need local processing Simply NUC has purpose built infrastructure to minimize cloud dependency and associated costs.

True or False? The myth busted

The myth that custom hardware is too expensive doesn’t hold up. While upfront costs may be higher in some cases, custom hardware can save businesses money in the long run through optimized performance, reduced operational costs and longer life cycles.

Instead of settling for generic solutions that don’t meet specific needs businesses should consider custom hardware as a strategic investment.

Useful Resources

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