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Foundry · Company record

TSMC

AI chips fabricated at TSMC per public disclosures. As of 2026-08-25, 39 chips across 12 designers.

NVIDIA14 chips

  • NVIDIA A10 Tensor Core GPU

    Ampere PCIe 150W inference GPU, 24 GB GDDR6. Successor to T4 in the mid-range inference tier.

  • NVIDIA A100 Tensor Core GPU

    Ampere-generation data-center GPU (SXM4 / PCIe), 40/80 GB HBM2e. The workhorse of AI training 2020-2023 and still dominant in the deployed base. TSMC 7nm.

  • NVIDIA B100 (Blackwell)

    Blackwell-architecture data-center GPU, 700W TDP variant. Shipped alongside B200 for existing HGX chassis compatibility.

  • NVIDIA B200 (Blackwell)

    Full-power Blackwell data-center GPU (~1000W TDP), 192 GB HBM3e. The core building block of the GB200 superchip.

  • NVIDIA DRIVE Thor

    Automotive-safety-certified Blackwell-based SoC for L4 AV compute; adopted by Volvo, Polestar, XPENG, and others.

  • NVIDIA GB200 Grace Blackwell Superchip

    Two B200 GPUs + one Grace CPU on a single board via NVLink-C2C. Base unit of the GB200 NVL72 rack. The flagship 2024-2025 AI-training package.

  • NVIDIA GB300 (Blackwell Ultra)

    Blackwell Ultra refresh of the Grace Blackwell superchip; announced GTC 2025 as the mid-cycle bump between Blackwell and the next Rubin architecture.

  • NVIDIA H100 Tensor Core GPU

    Hopper-architecture data-center AI GPU (SXM5 / PCIe). Dominant training + inference accelerator 2023-2024; 80 GB HBM3, ~700W TDP. Fabricated by TSMC on the 4N process.

  • NVIDIA H200 Tensor Core GPU

    Hopper refresh with 141 GB HBM3e (from 80 GB); the mid-generation memory-bandwidth bump between H100 and Blackwell.

  • NVIDIA Jetson AGX Orin

    Ampere-based embedded AI compute module (32-64 GB), the dominant robotics-side Nvidia SKU 2022-2025 before Jetson Thor ramp.

  • NVIDIA Jetson AGX Thor

    Blackwell-based embedded AI compute module aimed at humanoid robots and autonomous machines; positioned as the successor to Jetson Orin.

  • NVIDIA L40S

    Ada Lovelace data-center GPU (48 GB), targeted at mid-range training + inference + generative AI serving. Positioned between H100 and A10 in the Nvidia stack.

  • NVIDIA T4 Tensor Core GPU

    Turing-generation 70W PCIe inference GPU, 16 GB GDDR6. Dominant cloud inference SKU 2019-2022; still heavily deployed on AWS EC2 g4 + GCP T4 instances.

  • NVIDIA V100 Tensor Core GPU

    Volta-generation data-center GPU (2017), first with Tensor Cores. Legacy but still deployed in older DGX-1 and HPC clusters.

Google7 chips

  • Google TPU v7 (Ironwood)

    Seventh-generation TPU announced Cloud Next 2025; positioned as Google's first inference-first TPU generation.

  • Google TPU v2

    Second-generation Cloud TPU (2017), first generation available on Google Cloud. Legacy.

  • Google TPU v3

    Third-generation Cloud TPU (2018), first liquid-cooled generation. Still available on Google Cloud alongside newer generations.

  • Google TPU v4

    Fourth-generation Tensor Processing Unit; optical-switch-networked pods, the foundation for PaLM / Gemini training through 2024. Fabricated by TSMC.

  • Google TPU v5e

    Cost-efficient TPU generation optimized for inference and smaller training runs; the mainstream Google Cloud TPU offering.

  • Google TPU v5p

    Performance-tier TPU v5 generation, aimed at large-model training; ~2x compute vs TPU v4.

  • Google TPU v6 (Trillium)

    Sixth-generation TPU announced May 2024; ~4.7x peak compute over TPU v5e. Backbone of Gemini 2.0-era training runs.

AMD5 chips

  • AMD Instinct MI210

    CDNA 2 PCIe accelerator (64 GB HBM2e), the mid-tier of the MI200 family. Broadly deployed in AMD-native HPC and inference clusters.

  • AMD Instinct MI250X

    CDNA 2 data-center accelerator (128 GB HBM2e), pre-MI300 flagship. Powers Frontier + Aurora exascale HPC systems; still deployed in HPC + some AI training clusters.

  • AMD Instinct MI300X

    CDNA 3 data-center accelerator with 192 GB HBM3, positioned as the primary alternative to Nvidia H100/H200 for LLM inference workloads. Fabricated by TSMC.

  • AMD Instinct MI325X

    CDNA 3 refresh of MI300X with 256 GB HBM3e; announced late 2024. Bridge product before the MI350 series.

  • AMD Instinct MI350 series

    CDNA 4 next-generation accelerator (MI350X, MI355X) with 288 GB HBM3e and FP4/FP6 support. Announced 2024; ramping through 2025-2026.

Amazon3 chips

  • AWS Inferentia 2

    Second-generation AWS custom inference accelerator; the Inf2 EC2 instance family. Sits alongside Trainium in AWS's LLM stack.

  • AWS Trainium (Trn1)

    First-generation AWS custom training accelerator, launched 2022 in the Trn1 EC2 instance family. Fabricated by TSMC.

  • AWS Trainium 2

    Second-generation Trainium announced re:Invent 2023; 4x training performance of Trn1. Backbone of the Anthropic Project Rainier deal.

Intel Corporation2 chips

  • Intel Gaudi 2

    Second-generation Habana Gaudi AI training accelerator, positioned as Intel's Nvidia H100 alternative before Gaudi 3. Powers Intel Developer Cloud AI instances.

  • Intel Gaudi 3

    Third-generation Habana-designed AI accelerator, positioned as the mainline Intel alternative to Nvidia H100 for training + inference.

Meta2 chips

  • Meta MTIA v1

    Meta Training and Inference Accelerator, first generation. Custom silicon for ranking + recommendation workloads before generative expansion.

  • Meta MTIA v2

    Second-generation MTIA, ~3x compute per package, doubled memory bandwidth. Announced April 2024.

Alibaba T-Head1 chip

  • Alibaba Hanguang 800

    Alibaba T-Head Hanguang 800 inference NPU (2019). Powers Alibaba Cloud vision + recommendation inference across taobao/tmall. First-generation Chinese hyperscaler custom silicon at scale.

Cerebras Systems1 chip

Other1 chip

  • Etched Sohu

    First transformer-only ASIC; sacrifices generality for extreme throughput on transformer inference workloads. Announced June 2024.

Qualcomm1 chip

  • Qualcomm QRB5165

    Qualcomm Robotics Platform SoC based on Snapdragon 865. Dominant chip in commercial drones (Skydio X10, others) and robotics. Integrated 15 TOPS NPU.

SambaNova Systems1 chip

  • SambaNova SN40L

    Reconfigurable Dataflow Unit optimized for enterprise LLM serving with large-parameter memory hierarchies.

Tesla1 chip

  • Tesla Dojo D1

    Tesla's custom training accelerator for FSD neural nets; deployed in Dojo ExaPOD compute tiles at Tesla training clusters. Fabricated by TSMC.

Rebuilt every 15 minutes. Chip → foundry map is hand-maintained in src/services/chips.ts. See every tracked foundry or every AI chip on record.