# AWS AI Chips: The Lingo You Need _Demystifying the hardware behind AI: Key terms like CPU, GPU, inference, and training are essential for understanding AWS AI chips._ **Updated:** 2026-08-22 **Published:** 2026-05-12 **Source:** https://www.startuphub.ai/ai-news/technology/2026/aws-ai-chips-the-lingo-you-need --- Artificial intelligence doesn't run on magic; it runs on silicon. As AI permeates everything from chatbots to recommendation engines, grasping the underlying hardware has never been more critical. Amazon, a major player in this space, is pushing its custom silicon strategy forward. We break down the essential terms to understand [AWS AI chips explained](https://www.aboutamazon.com/news/aws/ai-chip-terms-explained?utm_source=rss). AI GrowthDriverAI permeates everything from chatbots to recommendation engines, driving demand for specialized hardwarePerformance GainsOutcomeFrom the article 5 mentionsAmazon's AWS Graviton processors are tailored for cloud computing, delivering significant performance gains.drivesAWS Custom SiliconCoreAmazon is pushing its custom silicon strategy forward to meet AI processing needsFrom the articleAmazon, a major player in this space, is pushing its custom silicon strategy forward.utilizesCPU & GPUContextCPU orchestrates complex AI workloads, GPU excels at parallel processing for training modelsFrom the article 2 mentionsCPU (Central Processing Unit): The workhorse for general computing, CPUs are increasingly vital for orchestrating complex AI workloads.AI GrowthDriverAI permeates everything from chatbots to recommendation engines, driving demand for specialized hardwaredrivesAWS Custom SiliconCoreAmazon is pushing its custom silicon strategy forward to meet AI processing needsFrom the articleAmazon, a major player in this space, is pushing its custom silicon strategy forward.utilizesCPU & GPUContextCPU orchestrates complex AI workloads, GPU excels at parallel processing for training modelsFrom the article 2 mentionsCPU (Central Processing Unit): The workhorse for general computing, CPUs are increasingly vital for orchestrating complex AI workloads.enablesAI TrainingContextFrom the article 4 mentionsGPU (Graphics Processing Unit): Originally for visuals, GPUs excel at parallel processing, making them a go-to for training massive AI models due to their ability to handle vast datasets simultaneously.AI InferenceContextUnderstanding inference is essential for grasping the underlying hardware behind AI applicationsFrom the article 2 mentionsInference: The application of a trained AI model to generate outputs, such as answering a query or creating an image.leads toPerformance GainsOutcomeFrom the article 5 mentionsAmazon's AWS Graviton processors are tailored for cloud computing, delivering significant performance gains.helpsDemystify AI HardwareOutcomeGrasping underlying hardware is critical for understanding how AI applications functionFrom the article 2 mentionsAs AI permeates everything from chatbots to recommendation engines, grasping the underlying hardware has never been more critical. ## The Core Components At the heart of AI processing are several types of chips, each with distinct roles. **CPU (Central Processing Unit):** The workhorse for general computing, CPUs are increasingly vital for orchestrating complex AI workloads. Amazon's [AWS Graviton](/ai-news/technology/2026/cpus-stage-ai-comeback) processors are tailored for cloud computing, delivering significant performance gains. **GPU (Graphics Processing Unit):** Originally for visuals, GPUs excel at parallel processing, making them a go-to for training massive AI models due to their ability to handle vast datasets simultaneously. **AI Accelerator:** These are chips purpose-built for AI tasks, offering superior performance and efficiency over general-purpose chips. AWS Trainium, for example, is designed for high-performance generative AI workloads, contributing to [Amazon's chip business surpassing $20B](/ai-news/technology/2026/amazon-s-chip-business-surges-past-20b). ## Key AI Processes Beyond the chips themselves, understanding the processes they enable is key. **Training:** This is the learning phase for AI models, involving feeding them enormous datasets. It's a highly compute-intensive process. **Inference:** The application of a trained AI model to generate outputs, such as answering a query or creating an image. Speed and cost per query are paramount here. ## Performance Metrics and Strategy Several metrics and strategic approaches define the AI chip market. **Price Performance:** A critical metric measuring the computing power delivered per dollar. Purpose-built silicon like Trainium and Graviton aim to offer superior price performance. **Purpose-built silicon:** This strategy involves designing chips from the ground up for specific workloads, optimizing for performance, efficiency, and cost, rather than attempting a one-size-fits-all solution. **Throughput:** This refers to the number of AI requests or operations a system can handle concurrently, essential for scaling AI services to millions of users. **Workload:** A general term for any computing task. Different AI workloads, like training versus inference, demand distinct hardware capabilities. **Cluster:** A network of interconnected chips and servers working as a single, powerful system. Training cutting-edge AI models often requires massive clusters. --- Original analysis from [startuphub.ai](https://www.startuphub.ai), the #1 AI startup directory. © StartupHub.ai. All rights reserved. You may not republish this article in full without a license. Search engines and AI research tools may crawl and summarize for reference. Bulk reproduction or model training on this content requires a license. See https://www.startuphub.ai/terms.