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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.

5 min read
Close-up of a complex computer chip with glowing circuits.
Understanding the hardware behind AI is crucial as companies like AWS develop specialized chips.· Amazon News
Visual TL;DR
AI GrowthDriver
AI permeates everything from chatbots to recommendation engines, driving demand for specialized hardware
Performance GainsOutcome
From the article 5 mentionsAmazon's AWS Graviton processors are tailored for cloud computing, delivering significant performance gains.
AWS Custom SiliconCore
Amazon is pushing its custom silicon strategy forward to meet AI processing needs
From the articleAmazon, a major player in this space, is pushing its custom silicon strategy forward.
CPU & GPUContext
CPU orchestrates complex AI workloads, GPU excels at parallel processing for training models
From the article 2 mentionsCPU (Central Processing Unit): The workhorse for general computing, CPUs are increasingly vital for orchestrating complex AI workloads.
AI GrowthDriver
AI permeates everything from chatbots to recommendation engines, driving demand for specialized hardware
AWS Custom SiliconCore
Amazon is pushing its custom silicon strategy forward to meet AI processing needs
From the articleAmazon, a major player in this space, is pushing its custom silicon strategy forward.
CPU & GPUContext
CPU orchestrates complex AI workloads, GPU excels at parallel processing for training models
From the article 2 mentionsCPU (Central Processing Unit): The workhorse for general computing, CPUs are increasingly vital for orchestrating complex AI workloads.
AI TrainingContext
From 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 InferenceContext
Understanding inference is essential for grasping the underlying hardware behind AI applications
From the article 2 mentionsInference: The application of a trained AI model to generate outputs, such as answering a query or creating an image.
Performance GainsOutcome
From the article 5 mentionsAmazon's AWS Graviton processors are tailored for cloud computing, delivering significant performance gains.
Demystify AI HardwareOutcome
Grasping underlying hardware is critical for understanding how AI applications function
From the article 2 mentionsAs AI permeates everything from chatbots to recommendation engines, grasping the underlying hardware has never been more critical.
Contents(3)

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.

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 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.

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.

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.

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