🧠 TPUv7 vs. NVIDIA: Can Google Break the CUDA Moat?
Anthropic’s Claude 4.5 Opus and Google’s Gemini 3—two of the world’s leading AI models—are trained on Google’s in-house TPUs (Tensor Processing Units) and Amazon Trainium chips. With Google now directly selling TPU hardware and expanding its cloud TPU offerings, the market is asking a bold question:
Is NVIDIA’s decade-long dominance finally under threat?
AI-era cost structures differ dramatically from past software eras. Hardware architecture now determines Capex, Opex, scalability, and gross margins. For companies deploying large-scale AI, infrastructure efficiency becomes a core competitive moat.
Google began designing AI-specific infrastructure in 2006 and accelerated TPU development in 2013 to avoid doubling its global data center footprint. By 2016, TPUs entered mass production—mirroring Amazon’s Nitro launch the same year, albeit aimed at a different computing paradigm.
This article explores Google’s commercial pivot with the TPU, why TPUv7 (Ironwood) has become NVIDIA’s most serious challenger, and how the industry is reacting.
⚡ I. Industry Shock: TPU Momentum Triggers a Market Chain Reaction #
The TPU’s rapid progress has alarmed competitors. OpenAI CEO Sam Altman acknowledged Gemini’s rise as a serious challenge, while NVIDIA quickly issued a reassuring statement:
“NVIDIA’s technology remains a generation ahead… capable of running all AI models with unmatched generality and flexibility.”
— NVIDIA Newsroom, Nov 26, 2025
Why NVIDIA is Defensive #
In recent months:
- Google DeepMind and Google Cloud have delivered major TPU advances
- Anthropic is deploying 1+ GW of TPU capacity
- Gemini 3 and Opus 4.5 both train on TPUs
- Potential customers include Meta, xAI, SSI, and OpenAI
TPU supply-chain stocks surged, while NVIDIA-related stocks stagnated.
TPU Supply Chain: GOOG, AVGO, LITE
Trainium Supply Chain: AMZN, MRVL
NVIDIA Supply Chain: NVDA, ORCL, MSFT
The “Circular Economy” Accusation #
Critics claim NVIDIA fuels a cycle where it funds AI startups who then buy NVIDIA GPUs—an unsustainable loop. NVIDIA responded:
- Only 3–7% of revenue involves strategic investments
- Disclosures are transparent
- Portfolio companies are growing rapidly
TPU Creates Price Pressure—Even Before Deployment #
SemiAnalysis reported:
OpenAI lowered its NVIDIA GPU TCO by ~30% simply by threatening to procure TPUs.
Competition works—even hypothetically.
🚀 II. Google’s Breakthrough in TPU Commercialization #
Historically, TPUs primarily served internal Google workloads. Though available on GCP since 2018, Google never pushed for true commercialization—until now.
Google has fully opened the TPU ecosystem using two models:
- GCP TPU leasing
- Direct sale of complete TPUv7 systems
This dual approach enables hyperscale partners (e.g., Anthropic) to reduce reliance on NVIDIA.
🏭 Anthropic’s Hardware Transformation #
From 2024–2025, Anthropic shifted dramatically:
- TPU and Trainium usage increased
- GPU usage shrank
DeepMind alumni inside Anthropic accelerated multi-hardware training of Sonnet and Opus 4.5.
💰 The 1 Million TPUv7 Deal #
Anthropic + Google = $50B+ total contract
Phase 1:
- 400k TPUv7 Ironwood chips delivered as finished racks
- $10B via Broadcom
- Fluidstack handles installation and testing
Phase 2:
- 600k TPUv7 leased through GCP
- $42B in RPO (Remaining Performance Obligations)
Google is also negotiating with Meta, xAI, SSI, and OpenAI.
🔌 Google’s Deployment Bottleneck: Power & Paperwork #
Google’s biggest constraint is data center onboarding, not technology.
Each new hosting partner requires a multibillion-dollar, multi-year MSA—taking up to 3 years.
To accelerate deployment, Google introduced an unprecedented model:
🧾 “Credit Guarantees” for Datacenter Providers #
Google provides off-balance-sheet guarantees to Neoclouds (e.g., Fluidstack).
If the provider can’t pay rent, Google steps in.
This unlocks new financing pathways for:
- New cloud providers
- Crypto miners pivoting to AI datacenters
Meanwhile, NVIDIA-funded cloud providers avoid TPUs due to tied interests—creating a vacuum new players are eager to fill.
🔥 III. TPUv7 Ironwood Technical Breakthroughs #
SemiAnalysis argues:
“System architecture matters more than chip microarchitecture.”
The TPU platform exemplifies this.
🧪 1. Model Validation #
- Gemini 3, a frontier LLM, was trained entirely on TPUs
- OpenAI has not completed a major new frontier model since GPT-4o (May 2024)
- TPU clusters show exceptional long-duration stability
- Google released Antigravity (code generation), competing directly with OpenAI Codex
⚙️ 2. Hardware Evolution: Ironwood Narrows the Gap with NVIDIA #
Historically, TPUs traded peak FLOPs for:
- Higher reliability (RAS)
- Lower downtime cost
- Realistic performance metrics (no FLOP inflation)
The LLM boom forced a shift.
TPUv6 and TPUv7 dramatically increased compute and bandwidth.
Summary of Recent Generations:
| Chip | Process | Peak FLOPs | Memory | Bandwidth |
|---|---|---|---|---|
| TPU v5p | N5 | 920 | 95GB | 1,250 GB/s |
| H100 | N4 | 1,980 | 80GB | 3,350 GB/s |
| TPU v6 | N5 | 2,800 | 96GB | 1,920 GB/s |
| GB200 | 4nm | 5,760 | 192GB | 8,000 GB/s |
| TPU v7 | 3nm | 5,120 | 192GB | 7,380 GB/s |
Trillium (v6) doubled peak FLOPs vs v5p on the same process node.
TPUv7 nearly matches NVIDIA GB200 in compute and HBM specs.
🌍 3. Real-World Value: TCO Wins #
Despite Broadcom premiums:
TPUv7 full-system TCO is ~44% lower than GB200.
For external customers via GCP:
- 30% cheaper than GB200
- 41% cheaper than GB300
Even with lower FLOP peaks, TPU’s performance-per-TCO dominates.
TPU’s advantage increases when Model FLOPs Utilization (MFU) improves:
- 40% MFU → 62% cheaper than GB300 per effective FLOP
- 15% MFU → parity with GB300 (30% MFU)
Anthropic is expected to exceed 40% MFU.
🧩 IV. TPU System Architecture: Google’s Secret Weapon #
Google’s strength comes from rack-to-datacenter vertical optimization.
🧊 1. Rack-Level Innovations #
Each rack contains:
- 16 TPU trays
- 8–16 CPU trays
- ToR switch
- PSU + BBU
Key features:
- Liquid cooling with dynamic flow control
- Vertical VRM placement + cold plate cooling
- Simple cabling (no complex backplanes like NVIDIA NVL72)
🔗 2. Inter-Chip Interconnect: 3D Torus + Optical Switching #
TPUv7 uses a 4×4×4 3D Torus (64 TPUs per rack):
- Within rack: DACs or PCB traces
- Between racks: 800G optics + Optical Circuit Switch (OCS)
Advantages of OCS:
- Zero packetization overhead
- Low latency
- High energy efficiency
- Flexible topology slicing (4 → 2048 TPUs)
Google also uses CWDM8 to send 800G over a single fiber, enabling full-duplex routing.
🌐 3. Datacenter-Level Network (DCN) #
OCS replaces the traditional Spine layer.
- Apollo (2022): 136×136 OCS for TPUv4 (4096 TPUs/pod)
- Ironwood (2025): 300×300 OCS enabling 147,000+ TPUs
No major re-cabling is needed for expansion.
🧰 V. Software Ecosystem: Closing the CUDA Gap #
CUDA remains NVIDIA’s strongest moat.
But Google is rapidly eroding that advantage.
🐍 1. Native PyTorch Support #
Launched October 2025:
- True PyTorch backend (no more
torch_xlahacks) - Eager execution works
- Full support for
torch.distributed, DTensor,torch.compile - Pallas kernels can be registered directly through PyTorch Inductor
This dramatically reduces migration friction.
⚙️ 2. TPU Support for vLLM & SGLang #
Google released:
- vLLM TPU preview
- TPU-optimized kernels (Paged Attention, GEMM)
- “Fully fused MoE” kernel with 3–4× speedup
📘 3. Remaining Gap: Open-Sourcing XLA #
TPU adoption is bottlenecked by:
- Closed XLA:TPU compiler
- Closed TPU runtime
- Closed MegaScale multi-cluster training code
- Sparse debugging documentation
Open-sourcing these components would supercharge TPU adoption—much like Linux and PyTorch ecosystems.
🥊 VI. Impact on NVIDIA: A True Challenger Emerges #
TPUv7 represents NVIDIA’s first true full-stack competitor:
Short-term: #
NVIDIA still leads with:
- CUDA
- Broad developer ecosystem
- GB300 performance
Long-term threats: #
- TPU cost advantage
- Major customers shifting (Anthropic, Meta; OpenAI may follow)
- PyTorch + vLLM support reducing switching cost
If the industry moves toward a TPU + GPU duopoly, AI compute costs will drop—accelerating large-scale AI deployment globally.