AI Chips
Explore the Best AI Chips for Your Needs
NVIDIA H100
NVIDIA's flagship GPU for AI, featuring Hopper architecture and transformative performance for training and inference in large language models.
Google TPU v4
Google's custom-designed AI accelerator that delivers exceptional performance for machine learning workloads in Google Cloud.
AMD MI300X
Advanced AI accelerator combining CPU and GPU capabilities, designed for large language model training and high-performance computing.
Intel Gaudi2
Purpose-built AI accelerator focusing on deep learning training workloads with competitive performance and cost efficiency.
AWS Trainium
Amazon's custom chip designed specifically for training deep learning models in AWS cloud environments.
Graphcore IPU
Intelligence Processing Unit designed for next-generation artificial intelligence workloads with innovative parallel processing architecture.
Cerebras CS-2
World's largest AI processor featuring a wafer-scale engine designed for deep learning training and inference.
Apple M3
Apple's latest silicon featuring enhanced Neural Engine capabilities for on-device AI and machine learning applications.
FAQ
What is this website?
This website is a directory of AI chips and accelerators used for artificial intelligence computing.
How do AI chips work?
AI chips are specialized processors designed to efficiently handle artificial intelligence workloads. They use parallel processing architectures and optimized circuits to perform the complex mathematical calculations required for AI training and inference.
Are AI chips better than regular CPUs?
For AI workloads, specialized AI chips significantly outperform regular CPUs. While CPUs are versatile general-purpose processors, AI chips are optimized specifically for machine learning tasks, offering better performance and energy efficiency.
Why should I use AI chips?
AI chips are essential for organizations running AI models, researchers developing new AI applications, and companies deploying AI solutions. They dramatically reduce processing time, lower energy costs, and enable more complex AI operations than traditional processors.
Can AI chips handle all types of AI workloads?
Different AI chips are optimized for specific tasks. Some excel at training large models, while others are designed for efficient inference. The best chip choice depends on your specific use case, whether it's deep learning, computer vision, or natural language processing.