AI Chips and Processors: GPU, TPU, NPU

AI Chips and Processors: GPU, TPU, NPU | AI Knowledge Hub

AI chip technology is rapidly evolving, with specialized processors designed to tackle the demands of modern machine learning. From powerful GPUs to dedicated TPUs and NPUs, understanding these architectures is key to optimizing your AI workflows.

mysimulator teamUpdated June 2026≈ 3 min read▶ Open the simulation

GPU, TPU, NPU and Specialized Processors

Specialized AI chips are the foundation of modern machine learning. From GPUs for training to...

…multi-layer neural networks.

Models: TPU v2, v3, v4, v5

Characteristics: Specialized for matrix operations.

Usage: Large models, production inference.

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Status: Research Stage

Performance: 312 TFLOPS (FP16)

Usage: Training, inference

Frequently asked questions

What is the AI chip ecosystem's support for machine learning frameworks?

The AI chip ecosystem provides robust support for popular machine learning frameworks.

Should I use A100/H100 GPUs or TPUs for training deep learning models?

A100 and H100 GPUs offer excellent performance for training, while TPUs are optimized for large-scale computations.

What GPU or TPU should I use for inference – RTX 4090 or A100?

RTX 4090 and A100 GPUs are both suitable for inference, with the A100 generally offering higher throughput.

What is an NPU (Neural Engine) used for in mobile devices?

An NPU, or Neural Engine, is a specialized processor integrated into mobile devices to accelerate AI tasks like image recognition and natural language processing.

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