TPU vs GPU: Google’s Chip Commercialization Accelerates, Can NVIDIA’s Moat Hold?

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As Google begins to attempt selling its self-developed AI chip TPU (Tensor Processing Unit) to a broader market, the "chip shadow war" that was once confined to the cloud is being brought to the forefront, posing a substantial challenge to Nvidia, the reigning AI chip giant.

According to analysis by tech media outlet The Information, Nvidia can no longer ignore the fact that the world's two most advanced AI models—from Google and Anthropic—were developed entirely or partially using Google's self-developed TPU chips, rather than Nvidia GPUs. This reality has prompted Meta, one of Nvidia’s largest clients, to seriously consider using Google's TPU to develop new models.

This means that the role of TPU has been upgraded from an "internal Google tool" to an alternative solution that major AI companies are seriously considering. According to previous analysis by Morgan Stanley, Google plans to produce over 3 million TPUs in 2026 and about 5 million in 2027, while Nvidia's GPU production is currently about three times that of Google's TPU.

Although a single TPU falls short of the strongest GPU, Google is leveraging ultra-large-scale clusters and better cost performance to challenge Nvidia's pricing power and market dominance. The real battlefield lies in ecosystems and business models—Nvidia locks users in with CUDA, while Google opens new doors with TPU + Gemini. Nvidia still holds a clear advantage in versatility and ecosystem maturity, but as more tier-one clients begin to "test the waters" with TPU, any slight change is rapidly amplified by the market.

Performance Comparison: Loses on single chip, wins on systems?

In terms of pure computing power, the most advanced TPU (codename Ironwood) achieves about half the FLOPS (floating point operations per second) of Nvidia’s Blackwell GPU.

But this does not mean TPU is at a disadvantage.

The Information notes that Google’s strategy is to amplify performance advantages through “clustering”: thousands of TPUs can be linked into a “super pod,” delivering outstanding cost and energy efficiency when training gigantic models. In comparison, a single Nvidia system can directly connect up to about 256 GPU chips, though users can expand size through additional networking hardware.

In the era of large models, it is hard to determine the winner simply by “single chip performance.” System-level design, interconnection capability, and energy efficiency are becoming new core metrics.

Key Difference: Software ecosystem remains Nvidia’s moat

The true moat for Nvidia lies not just in hardware, but in its deeply embedded CUDA software ecosystem.

The Information article states that for customers already running AI using Nvidia’s CUDA programming language, renting Nvidia chips is more cost-effective. Developers who have the time and resources to rewrite their programs can save costs by using TPUs.

For TPU customers like Anthropic, Apple, and Meta who have strong technical capabilities, using TPUs is relatively less challenging, as they are skilled at developing server chip software for AI applications. TPU is especially cost-efficient when running Google’s Gemini models optimized for it.

However, software compatibility remains the main challenge for TPU. TPUs only smoothly cooperate with specific AI software tools like TensorFlow, while the majority of AI researchers prefer PyTorch, which runs better on GPUs. Several engineers point out that if developers spend time writing custom software to fully tap into GPU capabilities, its performance can surpass that of TPUs.

Cost Battle: TPU is not “cheap”

In terms of manufacturing cost, there is actually little difference between TPU and GPU. Ironwood uses a more advanced and expensive manufacturing process than Blackwell, but due to its smaller chip size, more TPUs can be cut from a single wafer, partially offsetting cost disadvantages.

Both use high-bandwidth memory (HBM), and Broadcom plays a critical role in their process and packaging—not only participating in packaging design, but also providing key IP such as SerDes (high-speed data transmission technology). Analysis institutions estimate that Broadcom has earned at least $8 billion from the TPU project.

It is worth noting that Nvidia’s current hardware business gross margin is as high as 63%, while Google Cloud’s overall margin is only 24%. This explains why Nvidia can maintain strong profitability even during price wars.

Production Capacity Game: TSMC’s “balancing act”

On the foundry side, TSMC will not bet all its production capacity on a single customer. Even with extremely strong demand from Nvidia, it is hard to obtain “unlimited supply.” This means there will always be room in the market for other solutions—including TPU.

According to Morgan Stanley's projections, Google plans to produce 3 million TPUs in 2026, and 5 million in 2027, perhaps even more. Currently, Nvidia’s GPU production is about three times that of TPU, but the gap is narrowing.

As supply diversifies, customers are naturally more willing to compare, negotiate, and spread risks.

Commercialization Challenge: Selling chips is harder than you think

The Information believes that if Google truly wants to sell TPUs externally at scale, it essentially needs to rebuild an entire industry chain—including server manufacturers, distribution networks, enterprise-level after-sales support, etc.—which is basically “replicating Nvidia.”

Additionally, if customers deploy TPUs in their own data centers, Google will lose some cloud service revenues (such as storage and database services), meaning: TPUs are unlikely to take a “low-price approach” in the future, but will instead make up revenue with other fees.

In other words, this is not a business where "cheaper always wins," but a complex strategic choice.

On a higher level, the significance of TPU for Google goes far beyond hardware revenue alone. More importantly: it can be used as leverage in negotiations with Nvidia; it helps promote Gemini and Google's AI ecosystem, granting Google greater autonomy in AI infrastructure. As long as customers are willing to have “one more option,” Nvidia will lose its absolute pricing power.

This, perhaps, is what Google really wants.

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