What are TPUs? Everything you need to know about Google's market-moving AI chips.

Executive Snapshot

• Google’s first commercial pod‑scale TPU, Ironwood v7 (also called TPU7x), delivers >40 exaFLOPS FP8 and 800 Gb inter‑chip bandwidth.

• It is positioned as a low‑latency inference engine for multimodal LLMs such as Gemini 3, targeting hyperscalers and large enterprises.

• Early market moves—Meta’s multi‑billion lease and Anthropic’s million‑TPU rental—signal a shift away from NVIDIA lock‑in.

• For data‑center architects, the key decision is whether to adopt a pure TPU stack or mix TPUs with GPUs for mixed training/inference workloads.

The commercialization of TPUs marks Google’s transition from an internal ASIC powerhouse to a cloud compute services competitor. For enterprises, this has three immediate business ramifications:

• Cost‑Per‑Token Advantage : Benchmarks show a 30–40 % reduction in inference cost per token for Gemini 3 on Ironwood versus NVIDIA’s Blackwell GPUs. In a high‑volume chatbot or agentic service, that translates to tens of millions of dollars saved annually.

• Vendor Diversification : Hyperscalers can now shift part of their inference load from NVIDIA to Google without moving away from the same cloud ecosystem (Google Cloud Platform). This reduces dependency on a single silicon vendor and mitigates supply‑chain risk.

• New Revenue Streams for Alphabet : The TPU service model—leveraging accelerator pods as a managed offering—has already driven a 31 % stock appreciation in 2025. Enterprises can anticipate similar upside if they partner with Google for on‑prem or hybrid deployments.

Decision makers must weigh these benefits against the maturity of tooling, software stack compatibility, and potential lock‑in to Google’s ecosystem.

Below is a step‑by‑step blueprint for architects looking to integrate Ironwood v7 into their AI pipelines. The guide assumes a hybrid cloud environment where on‑prem or edge nodes coexist with GCP services.

Training‑Light, Batch‑Oriented

: Fine‑tuning large models or training from scratch on internal data sets. For these, v5e/v6 TPUs still dominate due to higher raw FLOPS per chip.

• Inference‑Heavy, Low Latency : Chatbots, real‑time translation, multimodal agents.

• Inference‑Heavy, Low Latency : Chatbots, real‑time translation, multimodal agents.

• Ironwood v7 (TPU7x) : 9,216 chips, >40 exaFLOPS FP8. Ideal for inference‑only workloads that need sub‑millisecond latency.

• Trillium (v6) : 4–5× performance uplift over v5e; balanced training/inference mix.

• Consider a tiered approach: use Trillium for large batch fine‑tuning and Ironwood for production inference.

The TPU pod is not an isolated silicon block; it lives within a hyper‑computer comprising:

• Axion CPUs : Low‑latency compute for control plane and non‑AI tasks.

• Google‑Fabric Interconnects : 800 Gb bandwidth between TPU chips, surpassing NVIDIA H100’s ~400 Gb.

• TPU‑Friendly SSDs : Optimized storage tiers reduce I/O bottlenecks during model parallelism.

Architects should design data pipelines that fully exploit the 800 Gb bandwidth—e.g., by partitioning models across multiple TPU chips with minimal cross‑chip communication overhead.

OpenAI GPT-4o and Claude 3.5

: While these models run primarily on NVIDIA GPUs, fine‑tuned versions can be ported to TPUs with minimal code changes due to XLA compatibility.

• TensorFlow 2.x & PyTorch via XLA : Google’s Accelerated Linear Algebra (XLA) compiler optimizes FP8 operations on TPUs.

• Model Parallelism Libraries : Mesh TensorFlow, GSPMD, and Megatron‑LM have TPU‑specific backends.

• Model Parallelism Libraries : Mesh TensorFlow, GSPMD, and Megatron‑LM have TPU‑specific backends.

Google Cloud’s TPU service offers granular billing per token and per second of compute. Use the following metrics:

• Throughput (tokens/sec) : Benchmark against baseline GPU performance to validate cost savings.

• Latency (ms) : Ensure SLA compliance for real‑time applications.

• Energy Efficiency (FLOPS/Watt) : Compare TPU vs. GPU in terms of TCO, factoring in cooling and power distribution costs.

Assume an enterprise runs a chatbot that processes 10 billion tokens per month on a Gemini‑3 model. Using the following simplified cost model:

Compute Platform

Cost per Token ($)

Total Monthly Cost ($)

NVIDIA Blackwell (GPU)

0.00012

1,200,000

Google Ironwood v7 (TPU)

0.00008

800,000

The TPU deployment saves $400,000 per month, or $4.8 million annually. With an estimated capital outlay of $30 million for a 9,216‑chip pod (including integration and cooling), the payback period is just under six months—excluding incremental savings from reduced data center footprint.

The TPU/Blackwell comparison hinges on three axes:

• Precision & Bandwidth : TPUs use FP8, delivering equivalent accuracy to FP16 while halving bandwidth. The 800 Gb inter‑chip link also doubles the data path compared to Blackwell’s ~400 Gb.

• Energy Efficiency : ASIC specialization gives TPUs a 30–40 % lower TCO per FLOP. Semi‑Analysis reports show a 25 % reduction in power consumption for equivalent throughput.

• Ecosystem Integration : NVIDIA’s CUDA ecosystem is mature, but Google’s XLA and TPU‑friendly storage tiers reduce software overhead by up to 15 % for inference workloads.

For enterprises heavily invested in the NVIDIA stack, a mixed approach—using GPUs for training and TPUs for inference—may offer the best of both worlds. However, pure TPU adoption is viable if the workload profile is inference‑centric and latency‑critical.

While the benefits are clear, several hurdles can impede smooth adoption:

Vendor Lock‑In

: Relying on GCP for TPU services may tie the enterprise to Google’s ecosystem.

Mitigation

: Explore on‑prem TPU deployments using Google’s On‑Prem TPU licensing program, or hybrid architectures that keep critical data in local storage.

Supply Chain Uncertainty

: Scaling TPU production relies on Samsung and SK Hynix for HBM3 memory.

Mitigation

: Negotiate multi-year supply contracts or diversify with alternative suppliers as they mature.

• Software Porting Effort : Existing PyTorch models may require XLA wrappers. Mitigation : Start with TensorFlow‑based models or use community‑maintained TPU adapters.

• Data Pipeline Bottlenecks : High‑bandwidth interconnects demand efficient data shuffling. Mitigation : Employ Google‑Fabric’s RDMA capabilities and optimize batch sizes to match TPU memory constraints.

• Data Pipeline Bottlenecks : High‑bandwidth interconnects demand efficient data shuffling. Mitigation : Employ Google‑Fabric’s RDMA capabilities and optimize batch sizes to match TPU memory constraints.

• Data Pipeline Bottlenecks : High‑bandwidth interconnects demand efficient data shuffling. Mitigation : Employ Google‑Fabric’s RDMA capabilities and optimize batch sizes to match TPU memory constraints.

The industry is already speculating on v8, which could push pod sizes beyond 12,000 chips and integrate AI‑CPU hybrids for even tighter inference loops. Key trends to watch:

NVIDIA Response

: NVIDIA is rumored to develop a dedicated inference ASIC—possibly named “TensorX”—to close the FP8 gap. Enterprises should monitor performance parity before committing to long‑term contracts.

• FP4 Precision : Early prototypes suggest FP4 may become viable for certain vision–language tasks, further reducing bandwidth.

• Edge TPU Deployments : Google’s on‑prem licensing program hints at smaller, lower‑power TPUs for regional data centers or edge nodes.

• Edge TPU Deployments : Google’s on‑prem licensing program hints at smaller, lower‑power TPUs for regional data centers or edge nodes.

Plan for Supply Chain Resilience

: Secure memory and silicon supply contracts early; consider diversified supplier footprints as the market matures.

• Run a Pilot on Ironwood v7 : Use a small pod (e.g., 512 chips) to benchmark your most critical inference workloads against existing GPU baselines. Capture cost, latency, and energy metrics.

• Adopt a Mixed Accelerator Strategy : Allocate high‑throughput training jobs to v5e/v6 TPUs or GPUs, while routing production inference to Ironwood pods.

• Negotiate Tiered Pricing with Google : Leverage early adopter discounts and volume commitments to secure favorable rates for multi‑year leases.

• Invest in XLA Training for Your ML Engineers : Upskill teams on TPU‑specific optimizations (FP8, inter‑chip communication patterns) to maximize performance gains.

• Invest in XLA Training for Your ML Engineers : Upskill teams on TPU‑specific optimizations (FP8, inter‑chip communication patterns) to maximize performance gains.

Google’s Ironwood v7 demonstrates that specialized AI hardware can transition from internal research chips to commercial, cloud‑based services that directly compete with NVIDIA’s GPU offerings. For enterprises, the decision is no longer about choosing between GPUs and TPUs in isolation; it’s about orchestrating a hybrid accelerator ecosystem that balances training throughput, inference latency, and total cost of ownership.

By piloting Ironwood v7 for high‑volume, low‑latency workloads, negotiating strategic contracts with Google, and investing in XLA expertise, enterprises can unlock significant savings while future‑proofing their AI infrastructure against the evolving silicon landscape.