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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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2022
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Mar 22, 2022
NVIDIA H100
The NVIDIA H100 is the first data-center GPU built on NVIDIA's Hopper architecture, announced in March 2022 with 80 billion transistors, HBM3 memory, FP8 Tensor Cores and a Transformer Engine aimed at large language models.
2019
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Aug 19, 2019
Cerebras Wafer Scale Engine
An AI processor from Cerebras Systems that keeps an entire silicon wafer as one chip, holding more than 1.2 trillion transistors and 400,000 cores; the first of a line that reached WSE-3 in 2024.
2016
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Apr 5, 2016
NVIDIA Tesla P100
The Tesla P100, announced by NVIDIA in April 2016, was the first data-center GPU on the Pascal architecture, and the first to combine stacked HBM2 memory, half-precision math aimed at deep learning and the NVLink interconnect.
2015
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2015
Google TPU (first generation)
Google's first-generation Tensor Processing Unit is a custom chip built to speed up neural-network inference in its data centers. Deployed in 2015 and revealed in 2016, it showed that dedicated accelerators could beat CPUs and GPUs on cost-performance.
2014
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Nov 2014
NVIDIA Tesla K80
NVIDIA's Tesla K80 is a dual-GPU data-center accelerator announced in November 2014, putting two GK210 chips, 24 GB of memory and up to 8.74 teraflops of single-precision performance on one 300 W board.
2010
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Nov 9, 2010
NVIDIA GeForce GTX 580
A consumer graphics card NVIDIA released in November 2010 on its Fermi architecture. Two of them, each with 3 GB of memory, later trained AlexNet, the network that won ILSVRC-2012 and which NVIDIA's timeline credits with sparking the era of modern AI.