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Foundry · Company record

TSMC

39 AI chips are made at TSMC, designed by 12 different companies.

Process-node breakdown

TSMC's AI chip work, grouped by which manufacturing process was used. Chips whose process isn't public show up under "unspecified".

  • unspecified11 chips
  • TSMC N76 chips
  • TSMC 4NP6 chips
  • TSMC N55 chips
  • TSMC 4N3 chips
  • TSMC N5 + N6 (chiplet)2 chips
  • Samsung 8N2 chips
  • TSMC 12FFN2 chips
  • TSMC N61 chip
  • TSMC N3 (chiplet)1 chip

Fastest chips TSMC makes (top 10)

Peak compute per chip, straight from each vendor's datasheet (FP8 where published, otherwise FP16/BF16).

  1. 162,500 TFLOPS
  2. 29,228 TFLOPS
  3. 39,000 TFLOPS
  4. 45,000 TFLOPS
  5. 54,600 TFLOPS
  6. 64,500 TFLOPS
  7. 73,500 TFLOPS
  8. 82,614 TFLOPS
  9. 92,614 TFLOPS
  10. 102,070 TFLOPS

All chips TSMC makes, by designer

NVIDIA14 chips

  • NVIDIA A10 Tensor Core GPUSamsung 8N

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

  • NVIDIA A100 Tensor Core GPUTSMC N7

    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)TSMC 4NP

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

  • NVIDIA B200 (Blackwell)TSMC 4NP

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

  • NVIDIA DRIVE ThorTSMC 4NP

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

  • NVIDIA GB200 Grace Blackwell SuperchipTSMC 4NP

    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)TSMC 4NP

    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 GPUTSMC 4N

    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 GPUTSMC 4N

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

  • NVIDIA Jetson AGX OrinSamsung 8N

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

  • NVIDIA Jetson AGX ThorTSMC 4NP

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

  • NVIDIA L40STSMC 4N

    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 GPUTSMC 12FFN

    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 GPUTSMC 12FFN

    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 v4TSMC N7

    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 MI250XTSMC N6

    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 MI300XTSMC N5 + N6 (chiplet)

    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 MI325XTSMC N5 + N6 (chiplet)

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

  • AMD Instinct MI350 seriesTSMC N3 (chiplet)

    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 2TSMC N7

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

  • AWS Trainium (Trn1)TSMC N7

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

  • AWS Trainium 2TSMC N5

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

Intel Corporation2 chips

  • Intel Gaudi 2TSMC N7

    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 3TSMC N5

    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 v2TSMC N5

    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 SN40LTSMC N5

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

Tesla1 chip

  • Tesla Dojo D1TSMC N7

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