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.
