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The chips that train and run AI models: GPUs, TPUs and other accelerators, and which models were trained on them.
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2025
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Jan 30, 2025
Intel Falcon Shores
Falcon Shores is an Intel chip that the company said in January 2025 it would use only as an internal test chip, never bringing it to market, while pointing to Jaguar Shores as its route into rack-scale AI data-centre systems.
2018
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Mar 27, 2018
NVIDIA DGX-2
NVIDIA's DGX-2, announced in March 2018, packed 16 Tesla V100 GPUs and a new NVSwitch interconnect into one server that NVIDIA described as the first to reach two petaflops for deep learning.
2017
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Sep 12, 2017
Apple A11 Bionic
Apple's 2017 smartphone chip, announced with the iPhone X: a six-core CPU, an Apple-designed three-core GPU and a dual-core neural engine built for on-device machine learning such as Face ID.
2016
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Sep 2016
NVIDIA Tesla P40
The NVIDIA Tesla P40 is a data-center GPU accelerator described in a September 2016 NVIDIA datasheet. Built on the Pascal architecture and aimed at deep learning inference, it pairs 24 GB of memory with 47 TOPS of INT8 throughput in a 250 W PCIe card.
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Apr 5, 2016
NVIDIA DGX-1
NVIDIA's DGX-1, unveiled in April 2016, packed eight Tesla P100 GPUs into a single chassis and was billed as the world's first deep learning supercomputer, turning accelerator count into one purchasable machine.
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Cerebras Wafer-Scale Engine 2
Cerebras's second-generation wafer-scale chip, presented as the largest AI processor ever made: 2.6 trillion transistors, 850,000 cores and 40 GB of on-wafer memory on a single wafer built on a 7-nanometer process.
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NVIDIA DGX Spark
NVIDIA's DGX Spark is a desktop-sized AI computer built on the GB10 Grace Blackwell Superchip, pairing a 20-core Arm CPU with a Blackwell GPU and up to 128 GB of unified memory for running large models locally.
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NVIDIA Tesla T4
The NVIDIA Tesla T4 is a 70-watt, single-slot GPU built on the Turing architecture and aimed at AI inference, pairing Tensor Cores with dedicated video transcoding engines in a card that slots into ordinary servers.