The big AI misconception: Most AI apps run on CPUs, not GPUs
While the industry touts GPUs as the foundation for AI applications, CPUs can still provide the reliable and quick processing that's necessary for most daily AI use cases.
If you've been following the AI boom, you've probably heard endless talk about GPUs as the powerhouse behind artificial intelligence. While that's true for training massive AI models, there's a misconception floating around: all AI runs on GPUs. The reality? Most of the AI you interact with daily is actually powered by good old-fashioned CPUs.
The GPU hype vs. reality
When OpenAI trains ChatGPT or Google develops new versions of Gemini, they use warehouses full of expensive GPUs. But what most people don't realize is that once these models are trained, using them is a different story. It's like the difference between building a car factory -- GPU territory -- and driving the cars that come off the production line, which is often CPU territory.
Real-world AI that runs on CPUs
Here are some examples of AI use cases that run on CPUs, not GPUs.
Your daily AI conversations
That customer service chatbot helping you track your package is probably running on CPUs. Most chatbots that handle routine inquiries don't need the massive parallel processing power of GPUs. They're designed to give quick, helpful responses to one person at a time, which is exactly what CPUs excel at.
AI assistants in apps
When you ask Siri a simple question, use voice-to-text on your phone or get smart suggestions in your email app, you're often experiencing CPU-powered AI. These applications prioritize speed and efficiency over raw computational power. Nobody wants to wait 10 seconds for their phone to understand "Call Mom."
Recommendation engines
What about when Netflix suggests your next binge-watch, Spotify creates your Discover Weekly playlist or Amazon shows you products you might like? These recommendation systems often run smoothly on CPUs. They process your individual preferences and behavior patterns; they don't crunch through billions of data points simultaneously.
Smart home devices
A smart thermostat that learns your schedule, security cameras that detect motion or voice assistants that respond to commands are typically CPU-powered. These devices need to be responsive, energy-efficient and cost-effective. GPUs would be overkill and raise your electricity bill.
Why CPUs make sense for everyday AI
Here are some reasons why CPUs are effective for daily AI use cases.
They're fast enough
For most real-world AI applications, CPUs provide enough processing power. When you're conversing with a chatbot or getting a product recommendation, you don't need supercomputer-level performance. You need something that works quickly and reliably.
They're everywhere
Every smartphone, laptop and server already has a CPU. This means companies can deploy AI features without investing in expensive, specialized hardware. It's like using the kitchen you already have instead of building a commercial restaurant kitchen to make breakfast.
They handle real life better
Real-world AI applications are messy. They need to juggle multiple tasks, handle interruptions and work alongside other software. CPUs are natural multitaskers, seamlessly switching between running an AI assistant, managing your calendar and keeping your video call running smoothly.
They're cost-effective
For businesses deploying AI at scale, CPUs often make more financial sense. A company that runs customer service chatbots for thousands of simultaneous conversations can serve those customers more cost-effectively with CPU-based offerings than by investing in GPU infrastructure that often sits idle.
The bottom line
The next time someone tells you that AI requires cutting-edge GPU technology, remind them that the AI revolution isn't just happening in research labs with million-dollar hardware budgets. It's happening in your pocket, in your apps and in everyday services you use without even thinking about it.
The most successful AI applications aren't necessarily the most technically impressive ones. They're the ones that solve real problems efficiently and affordably. More often than not, that means running on the humble, reliable CPU that's been powering our digital world for decades.
The future of AI isn't just about building bigger, more powerful systems. It's about making AI so accessible and efficient that it becomes invisible, seamlessly integrated into everything we do. And CPUs are making that future possible, one everyday interaction at a time.
Stephen Catanzano is a senior analyst at Omdia, where he covers data management and analytics.
Omdia is a division of Informa TechTarget. Its analysts have business relationships with technology vendors.
