What Kind of Hardware Powers AI, and Why Does It Matter?
AI models require immense computing power to operate. From the training phase—where models learn from huge datasets—to the inference phase—where they respond to your prompts in real time—the right hardware makes a big difference in performance, cost, and accessibility. In this section, we’ll look at the key types of chips and infrastructure that make AI run, from cloud data centers to devices in your pocket. All of these concepts apply to how AI shows up for you at Gloo, with the exception being that Gloo does not currently offer any on-device or offline AI solutions.Inference Engine
What it means: An inference engine is the part of the AI system that runs the model and produces outputs in real time, based on your input. Why it matters: Inference is what happens when a model is already trained and ready to answer your questions, generate content, or complete tasks. Efficient inference determines how fast, accurate, and affordable those interactions are.AI Chips
What it means: AI chips are computer processors designed specifically for the kinds of math operations AI models rely on—especially matrix multiplication. Why it matters: They’re faster and more power-efficient than general-purpose CPUs. AI chips make it possible to train and run models at scale without breaking the bank.Neural Processing Units (NPUs)
What it means: NPUs are specialized processors built into some phones, tablets, or laptops to handle AI tasks locally. Why it matters: NPUs enable real-time AI features—like voice transcription, image enhancement, or translation—directly on your device, with better speed and privacy.Graphics Processing Units (GPUs)
What it means: Originally designed for rendering graphics, GPUs are now the standard hardware for training and running AI models due to their ability to handle many operations in parallel. Why it matters: GPUs are the workhorses behind most large-scale AI systems. Companies like NVIDIA dominate this space.TPU (Tensor Processing Unit)
What it means: A TPU is a custom chip developed by Google specifically for machine learning workloads. Why it matters: TPUs offer high performance for Google’s internal AI operations and are also available in its cloud platform, giving developers another powerful option for model training and inference.Edge AI
What it means: Edge AI refers to running AI models on local devices—like smartphones, drones, or IoT devices—rather than sending data to the cloud. Why it matters: Edge AI reduces latency, improves privacy, and allows offline functionality. It’s useful for real-time tasks in healthcare, robotics, or smart home devices.On-device AI
What it means: A subset of edge AI, on-device AI specifically refers to models that run directly on personal devices, often using NPUs. Why it matters: It allows AI features—like predictive text or facial recognition—to happen without internet access, improving speed and keeping sensitive data local.Next Up: How Do We Know if an AI Model Is Good? In the next section, we’ll answer: “How do researchers measure model performance, and what makes one model better than another for a specific task?”