Google TPU: From “Internal Secret Weapon” To An AI Weapon That Can Shake NVDA?  

Google TPU is a dedicated accelerator that Google built in‑house for AI. It started out being used only for internal services, and has now become both a cloud product and an independent AI chip business, changing the rules of the entire AI infrastructure game. In this process, TPUs both help Google lower the cost of its own AI, and at the same time move toward external sales and cloud supply, posing a substantial long‑term threat to Nvidia, which almost monopolizes AI chips today, and potentially making future AI products “cheaper, more power‑efficient, and more everywhere.”  

What Is A TPU, And Why Does Google Need Its Own Chip?  

A TPU (Tensor Processing Unit) is not a general‑purpose GPU. It is an ASIC chip designed for deep‑learning core workloads such as matrix multiplication and vector operations, and is especially friendly to today’s Transformer‑based models like Llama and Gemini. In the early days, TPUs were mainly hidden inside Google’s data centers, silently accelerating Search, YouTube and ad recommendation systems. Outsiders only saw that “Google’s AI is very strong” but could not see the hardware differences behind it. In the last few years, Google began opening up Cloud TPUs to enterprise customers and, after 2025, has been pushing its latest TPU product lines (such as Trillium and Ironwood / TPU v7) into larger‑scale cloud markets, even planning to let customers deploy them directly in their own data centers.  

From v1 To Ironwood: Built For The LLM Era  

If you look at the evolution of TPUs over a longer horizon, it actually follows a very “Google‑style” path:  

• 2016: TPU v1, inference only, used in internal products.  
  
  
• 2017–2020: TPU v2 / v3 / v4, adding training capability, pod architecture and liquid cooling, greatly increasing scale and energy efficiency.  
  
  
• After that: v5e / v5p and Trillium, starting to emphasize performance per watt and large‑scale distributed training.  

The main character in 2025 is the seventh‑generation TPU, codenamed Ironwood, also known as TPU v7. Ironwood is designed for the “age of inference,” where the focus is no longer only on training, but on running ultra‑large LLMs and reasoning models at massive scale “both fast and power‑efficient.” A single Ironwood TPU offers FP8 compute at several petaFLOPS, more than one hundred GB of HBM and extremely high bandwidth, and the official pod configuration is astonishing: up to 9,216 chips in one pod, with total compute comparable to a supercomputer purpose‑built for generative AI. The goal of this design is very direct: to provide a stable training and inference platform for ultra‑large models like Gemini 2.5, and at the same time allow external customers to spin up AI workloads on Google Cloud using “ultra‑high‑density plus liquid cooling,” without having to figure out hardware tuning details themselves.  

Energy Efficiency And Carbon Emissions: The Invisible Key To AI Expansion  

Another key aspect of Ironwood is energy efficiency and carbon emissions. In a 2025 study, Google reported that from TPU v4 to the sixth‑generation Trillium, the “carbon efficiency” of AI workloads had already improved by about three times — in other words, the same amount of compute now produces only about one‑third of the emissions it did before. On top of that, Ironwood further improves performance per watt compared with the previous generation, meaning that in just a few generations, the cumulative effect on perf/W is dramatic. For customers who care about ESG and need to run large‑scale LLMs, this improvement in energy efficiency directly translates into electricity bills, carbon footprints and the very practical question of “can we convince the board to keep increasing the AI budget?” From a more macro perspective, as mainstream AI chips become more power‑efficient and easier to run on green energy, governments and regulators will have a more favorable attitude toward large‑scale AI projects, which in turn accelerates AI adoption.  

TPU vs GPU: More Than Just A Spec Sheet Battle  

By comparison, the GPU world is still dominated by Nvidia. Nvidia bet on AI early through its CUDA ecosystem, creating deep integration between hardware and software, so that most AI teams still see “buy GPUs, write CUDA or use compatible frameworks” as the default path. This early‑mover advantage has produced today’s very high market share for Nvidia in AI GPUs. However, as high‑end TPUs like Ironwood gradually catch up with — and in specific scenarios even surpass — Nvidia’s latest Blackwell GPUs in performance and efficiency, more analysts have begun to see Google TPUs as the main rival “capable of standing toe‑to‑toe with Nvidia in the AI ASIC space,” especially in cloud inference and internal services.  

From a business‑model perspective, much of TPU’s impact on Nvidia comes from “how it is packaged,” not just from “chip specs.” Nvidia’s core model is to sell GPUs (plus software licenses and platform services), and let cloud providers and enterprises build their own services. Google, on the other hand, deeply integrates TPUs into its Cloud products and turns them into one‑stop offerings like “AI Hypercomputer” and “Gemini API,” so customers are effectively buying a full stack of compute + storage + network + models + tooling, rather than just a single card. Once Google can use Ironwood to drive down the cost of its own services, then pass part of that cost advantage to customers via cloud pricing and bundle deals, Nvidia will feel pressure in the cloud market. Even if TPUs never fully replace GPUs, as long as they can capture a slice of inference workloads, the long‑term growth trajectory of Nvidia will face “margin compression at the edges.”  

Google: From Big Customer To Direct Competitor  

The more sensitive change is the role reversal: Google is no longer just one of Nvidia’s biggest customers, but is actively trying to “take back” this profit pool. Some reports say Google’s internal goal is to use wider TPU adoption to capture a portion of Nvidia’s annual AI chip revenue; this is no longer just a strategic backup, but a real fight over income. Recently there have also been reports that Google is in talks with other large tech companies (such as cloud and social‑media giants) about TPU partnerships, and may even have them adopt TPUs to replace part of their Nvidia GPU demand, leading to market interpretations that “Nvidia’s strongest rival may actually be Google.”  

At the same time, Google is no longer satisfied with only renting out TPUs inside its own cloud, and has started discussing placing TPUs directly in other companies’ data centers. Some reports suggest that Google is willing to sign multi‑year minimum‑revenue guarantees with data center operators, ensuring that they are not afraid to bet on TPUs just because they “worry about not having customers.” In essence, this copies the previous bundling strategies between Nvidia and hyperscale customers — and even counter‑attacks Nvidia on its home turf. If this “external TPU” model succeeds, then in the future, when enterprises consider AI infrastructure, their options will no longer be just “buy Nvidia cards and build it yourself,” but also “work with Google, get a full rack of TPUs plus software stack plus long‑term price guarantees.” The balance of bargaining power across the supply chain would look very different from today.  

Long‑Term Impact On AI Itself  

So what does all this mean for the long‑term development of AI itself? First, the compute cost curve — especially for inference — will be pushed down. With the performance‑per‑watt improvements of Ironwood‑class TPUs, plus software optimizations like vLLM and Pathways, Google claims that TPUs can offer better cost‑performance in many LLM inference scenarios, which is crucial for teams that want to “embed AI functionality into every corner of their products.” As the cost of every thousand queries drops to something closer to “a fraction of a cup of coffee,” product thinking can shift from “use AI carefully and sparingly” to “assume AI is always on and every workflow can consult it,” providing the foundation for an explosion of agent and copilot‑style applications.  

Second, better energy efficiency and lower carbon emissions make it more feasible for AI to operate at massive scale over the long term without being quickly choked by “energy and environmental pressure.” Google’s research indicates that in just two generations, TPUs have roughly tripled the carbon efficiency of AI workloads, and that operational electricity is the main contributor to lifecycle emissions. This implies that “hardware energy efficiency + clean energy” will be the core combination that determines whether AI expansion is sustainable. When cloud providers can show customers and regulators hard numbers that “carbon per unit of compute is decreasing,” resistance to AI adoption among big government projects, financial institutions and multinationals will be much lower, enabling more real‑world use cases to land.  

Third, the ecosystem will become more fragmented but also more specialized: Nvidia will continue to dominate the general‑purpose GPU + CUDA developer ecosystem, while Google TPUs will form a “highly integrated, cost‑optimized parallel universe” inside Google Cloud, certain partner data centers and Google’s own product lines, and AWS will push its own Trainium / Inferentia path. For developers and enterprises, the likely future is multi‑platform coexistence: train models on Nvidia GPUs, run large‑scale inference on TPUs or other ASICs, and route workloads across different clouds depending on the scenario. The result is that AI innovation may move even faster as hardware vendors compete and drive prices down, but engineering teams will also have to learn to build abstraction layers across platforms to avoid being excessively locked in to any single supplier.  

For investors, the rise of Google TPUs does not mean Nvidia will immediately fall out of favor. In the short term, Nvidia still enjoys high margins, strong demand and a very sticky ecosystem; TPUs are more like a competitor that slowly eats away at edge segments rather than a one‑shot killer. But in the long run, as Google, AWS and other clouds and large customers roll out their own AI chips, if Nvidia’s pricing power and market share are gradually diluted year after year, its valuation will need to reflect a world where it is no longer the only choice. In this new landscape, TPUs represent a path toward more energy‑efficient, more cloud‑native and more competitive AI infrastructure — and that path is already taking shape.