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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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2024
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Mar 18, 2024
NVIDIA B200 (Blackwell)
NVIDIA's data-center GPU for the Blackwell generation, announced in March 2024 as the successor to Hopper, built from two joined dies and paired with NVLink and FP4 support for large-scale AI training and inference.
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.
2020
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May 14, 2020
NVIDIA A100
NVIDIA's A100 is a data-center GPU, the first built on the Ampere architecture. Announced on 14 May 2020, it introduced third-generation Tensor Cores, the TF32 format and Multi-Instance GPU, and became a standard building block for training large language models.
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2020
Google TPU v4
Google's TPU v4 is a machine-learning chip and supercomputer deployed since 2020, built into 4,096-chip pods with optically reconfigurable networking; PaLM and Gemini models were trained on it.
2017
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May 10, 2017
NVIDIA Tesla V100
NVIDIA's first data-center GPU built on its Volta architecture, announced in May 2017. It added Tensor Cores, dedicated matrix-multiply units; OpenAI later trained its GPT-3 models on V100s.
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.
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NVIDIA L40S
The NVIDIA L40S is a data-centre GPU on Nvidia's Ada Lovelace architecture, pairing 48 GB of ECC memory with AI and graphics throughput in a passive, dual-slot PCIe Gen4 card that omits NVLink and MIG.