Hardware & compute Compute

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.

Google TPU v2 is the second-generation Tensor Processing Unit announced in May 2017. It added floating-point support and the bfloat16 format, and scaled to 256-chip pods rated at 11.5 petaFLOPS.

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.

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.

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.

Cerebras's third-generation wafer-scale AI chip, announced in March 2024: 4 trillion transistors on one 46,225-square-millimetre piece of silicon, built to keep very large models in a single memory space.

NVIDIA's GB200 NVL72 is a liquid-cooled rack that links 72 Blackwell GPUs and 36 Grace CPUs into a single NVLink domain aimed at trillion-parameter AI. Announced at GTC in March 2024, it is the building block of NVIDIA's Blackwell-generation DGX SuperPOD.

SK hynix's 12-layer HBM3E, announced in mass production on 26 September 2024, was the first 12-die high-bandwidth memory stack: 36 GB in a package no thicker than the eight-layer version, running at 9.6 Gbps.

Huawei CloudMatrix384 is a Huawei Cloud service instance built on Atlas 900 A3 SuperPoDs, each linking up to 384 Ascend 910C chips so that they work as one computer.

TPU7x (Ironwood) is Google's seventh-generation TPU: 192 GB of high-bandwidth memory per chip, FP8 matrix units, 9,216 chips per pod, and a programming model that exposes two devices per chip.

Huawei's Ascend 910C is a data-center AI chip in the 910 series, built on the Da Vinci architecture. It reached the market in the first quarter of 2025 with 800 TFLOPS of FP16 compute and 128 GB of HBM.

A rack-scale NVIDIA system announced in March 2025 that links 72 Blackwell Ultra GPUs and 36 Grace CPUs into one unit, rated at 1.5 times the AI performance of GB200 NVL72.

SK hynix said on 19 March 2025 that it was the first to supply 12-layer HBM4 samples, a stack offering more than 2 TB of data per second and 36 GB of capacity, over 60% faster than HBM3E.

AMD's data-center GPU for AI, introduced in June 2025 on the CDNA 4 architecture, with 288 GB of HBM3E memory and very high low-precision throughput aimed at generative AI inference, positioned against Nvidia's Blackwell generation.

Huawei's Ascend 950 is a two-chip AI series announced in September 2025, pairing a shared die with Huawei's own high bandwidth memory, up to 2 PFLOPS in low precision formats and 2 TB/s interconnect.

AWS Trainium3 is AWS's first 3-nanometer AI chip, delivering 2.52 petaflops of FP8 compute per chip and scaling to 144 chips in a Trn3 UltraServer, available through Amazon EC2 from December 2025.[^1][^2]

NVIDIA Rubin is NVIDIA’s six-chip AI platform and the successor to Blackwell, announced at CES 2026. It targets lower-cost AI token generation, with partner products and cloud instances scheduled for 2026.

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.

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.

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.

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.

Google's third-generation Tensor Processing Unit, introduced in 2018, with doubled HBM capacity, higher peak throughput, lower power draw and liquid cooling for denser pods.

Huawei's Ascend 910 is an AI training processor launched in 2019 alongside the MindSpore framework, rated at 256 teraflops FP16 and used to train the PanGu-α language models on clusters of thousands of chips.

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.

NVIDIA's third-generation DGX system, unveiled on 14 May 2020: eight A100 GPUs, 320 GB of GPU memory, five petaflops of rated AI performance, and the ability to split into as many as 56 instances.

Cambricon's third-generation cloud AI chip, released in 2021: a 7 nm chiplet design with two compute dies, 256 TOPS at INT8, LPDDR5 memory and multi-chip interconnect, positioned for cost-effective inference.

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.

NVIDIA's A800 is a data-center GPU that went into production in 2022 as a China-market alternative to the A100, with a reduced chip-to-chip transfer rate to meet US export-control thresholds.

NVIDIA's fourth-generation DGX system, announced in March 2022, packs eight H100 GPUs linked by NVLink plus BlueField-3 networking, and is rated at 32 petaflops of FP8 AI performance for enterprise AI infrastructure.

Biren's general-purpose GPU, released in August 2022, packages two dies on a 7 nm process with 77 billion transistors; the company claimed PFLOPS-level single-chip compute, a first for a Chinese firm.

Cambricon's MLU 590 is a 7 nm cloud AI chip released in 2023 for both training and inference on models with hundreds of billions of parameters, and the centrepiece of the company's fifth-generation MLUarch line.

Huawei's Ascend 910B is a 2023 volume-production AI chip rated at 320 TFLOPS of FP16, with 32 GB of HBM2e memory, 1.6 TB/s of bandwidth and a 310 W envelope on a 7-nanometer domestic process.

NVIDIA's China-specific data-center GPU, built to stay inside US export controls. Cut down from the H100 in core count and performance, it was still the most advanced NVIDIA chip legally sold in China until April 2025.

The H800 is a China-market version of NVIDIA's H100 data-center GPU, adapted to fit the US export rules of October 2022 by cutting chip-to-chip transfer speed. It was later used to train DeepSeek-V3 and was covered by tighter US controls in October 2023.

NVIDIA's H200, announced in November 2023, is a data center GPU that was the first to use HBM3e memory, offering 141 GB at 4.8 TB/s — nearly double the A100's capacity — and roughly double the H100's inference speed on Llama 2 70B.

Azure Maia 100 is Microsoft's first custom AI chip, announced in November 2023 and built to train and run large language models inside its own Azure data centres rather than being sold.

Google TPU v5p is a 2023 TPU generation with 459 teraflops of peak BF16 and FP8 compute per chip, 95 GiB of HBM, and 8,960 chips per pod.

AMD's data-center GPU for generative AI, released in December 2023. Built on the CDNA 3 architecture with a chiplet design, it leads with 192 GB of HBM3 memory, which AMD contrasted with the 80 GB on Nvidia's H100.

Intel Gaudi 3 is an AI accelerator introduced in 2024, built from two 5 nm compute dies with 128 GB of HBM2e and 1,835 TFLOPS of BF16 and FP8 matrix throughput, plus integrated 200GbE networking on its OAM module.

The Kunlunxin P800 is a Chinese AI accelerator launched in 2024, the third generation of Kunlunxin's line, built on its own XPU-P architecture and used in large training clusters and in customer deployments.

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.

Meta's second-generation in-house AI chip, published on 10 April 2024, built on TSMC's 5-nanometer process and aimed at the ranking and recommendation models Meta runs in its own data centers.

A data-center GPU announced by AMD on 10 October 2024, built on third-generation CDNA with 256 GB of HBM3E memory at 6 TB/s and aimed at models with a trillion parameters.

Google TPU v6e, also called Trillium, is a Google Cloud AI accelerator whose published specifications cover per-chip BF16 and FP8 compute, high-bandwidth HBM, and pod-scale interconnects for large training jobs.

AWS Trainium2 is Amazon Web Services' second-generation machine-learning chip, sold through 16-chip EC2 Trn2 instances and 64-chip Trn2 UltraServers, and pitched by AWS as offering better price performance than comparable GPU instances.

Hygon's Shengsuan No. 3 is a GPGPU-architecture deep computing processor that reached the market in 2025, built on a 7 nm chiplet design with 1.8 TB/s of HBM bandwidth and a CUDA-like software stack.

Moore Threads' flagship training-and-inference GPU card, built on the PH100 chip and the Pinghu architecture, rated at 1,000 TFLOPS of dense AI compute with 80 GB of memory, and the company's main shipping product in 2025.

AMD Instinct MI350X is a data-center GPU in AMD's MI350 series, built on the CDNA 4 architecture with up to 288 GB of HBM3E memory, up to 8 TB/s of bandwidth and a 1,000 W board power.

Huawei's Ascend 970 is a planned AI chip announced in September 2025 as the last entry on the company's published roadmap, targeted for the fourth quarter of 2028 with roughly double the compute and wider interconnect and memory bandwidth than the Ascend 960.

The Ascend 960 is a Huawei AI chip on the company's three-year roadmap, slated for the fourth quarter of 2027 with twice the compute, memory and interconnect ports of the Ascend 950 and support for Huawei's HiF4 4-bit format.

The AMD Instinct MI400 is AMD's next-generation data-center GPU for AI, expected in 2026 with 432 GB of HBM4 memory, and designed to be deployed inside AMD's Helios rack-scale system.

Cambricon's flagship MLU 690, in mass production since early 2026, is a dual-die accelerator with 196 GB of HBM3 memory and more than 700 TFLOPS of FP16 compute, aimed at very large-scale training workloads.

Microsoft's Maia 200 is an inference accelerator introduced on 26 January 2026, built on TSMC's 3-nanometer process with 216 GB of HBM3e memory and more than 10 petaflops at FP4, aimed at serving models such as OpenAI's GPT-5.2.

Zhenwu 810E is an AI processor designed by Alibaba's chip team T-Head, listed in January 2026 with 96 GB of high-bandwidth memory, aimed at training, inference and autonomous driving, and already deployed in large clusters on Alibaba Cloud.

Google TPU v5e is a 2023 machine-learning accelerator chip with one TensorCore per chip, 197 TFLOP of bfloat16 peak compute, and 256-chip pods, positioned for both training and serving.

The GH200 Grace Hopper Superchip is an NVIDIA module that puts a 72-core Arm CPU and a Hopper GPU in one package joined by NVLink-C2C, pairing fast HBM with up to 480 GB of CPU memory for AI and technical computing.

AWS Trainium is a chip built by Amazon Web Services for deep learning training, sold through EC2 Trn1 instances that AWS says cut training costs by up to 50% compared with comparable instances.

AWS Inferentia2 is Amazon Web Services' second-generation chip built for inference, offering 32 GB of high-bandwidth memory, up to 190 TFLOPS in FP16 and up to 12 chips per EC2 Inf2 instance.

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.

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.

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.

MTIA v1 is Meta's first-generation in-house AI inference accelerator, built on TSMC's 7 nm process with 128 MB of on-chip memory and designed for deep learning recommendation models.

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.

Intel Gaudi 2 is a data-centre AI accelerator with 96 GB of HBM2e memory, 432 TFLOPS of BF16 compute and 24 tensor processor cores, built to run the matrix arithmetic behind large neural networks.

GroqChip is Groq's AI processor, built for fully deterministic execution with no run-to-run variation. It pairs a 14-nanometer die with 230 MB of on-die SRAM, up to 750 INT8 TOPS and RealScale chip-to-chip links, and ships both as a chip and on the PCIe GroqCard.

AWS Inferentia is a custom chip designed by AWS for running trained machine-learning models, sold to customers through EC2 Inf1 instances. It aims to make inference cheaper and faster than general-purpose instances.

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.

The Xicloud C600 is a MetaX data-center GPU built on the company's own GPGPU architecture, offering very large ECC-protected memory, MetaXLink scaling up to 128-GPU supernodes, a 1,000 W OAM 2.0 module, and air or liquid cooling.

SambaNova SN40L is an AI chip that SambaNova calls a Reconfigurable Dataflow Unit, pairing dataflow-style parallel execution with a three-tier memory system spanning on-chip SRAM, co-packaged HBM and DDR DRAM.

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.

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.

The AMD Instinct MI100 is a GPU accelerator built on AMD's first-generation CDNA architecture. It pairs 32 GB of HBM2 memory with peak throughput ratings for double-, single-, and half-precision math.

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.

The NVIDIA A10 is a data-center GPU built on the Ampere architecture that combines graphics, video and AI inference in a single-slot PCIe card with 24 GB of memory, aimed at mainstream enterprise servers.

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.

A data-center GPU on NVIDIA's Ampere architecture, aimed at AI inference and mainstream enterprise workloads, with 24 GB of HBM2 memory, a 165 W power ceiling and the ability to split into as many as four MIG instances.

Blackhole is Tenstorrent's AI accelerator chip; Galaxy Blackhole is a server holding 32 of them, rated at 23 PFLOPS of block FP8, with Ethernet links wiring chips and clusters together.