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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.
2021
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Nov 8, 2021
AMD Instinct MI250X
AMD's data-center GPU accelerator launched on 8 November 2021, built on the CDNA 2 architecture with 220 compute units, 128 GB of HBM2e memory, and strong double-precision throughput alongside low-precision AI formats.
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.