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Can Google’s Ironwood TPU Dethrone Nvidia’s AI Empire?

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sleepeeg3

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247wallst said:
Ironwood is the seventh version of Google’s TPUs that are designed specifically for handling artificial intelligence workloads. Unlike chips that do a bit of everything, Ironwood zeroes in on the core calculations needed for AI, making it great for building huge AI models and running them quickly in everyday uses, like chatbots or smart assistants.

In terms of speed, Ironwood is over four times faster than its previous model, Trillium, and up to 10 times quicker than the one before that. It comes with 192 gigabytes of fast-access memory per chip — six times more than Trillium — which helps process data without slowdowns. Google says it is its most power-saving TPU so far, with efficiency gains of nearly 30 times compared to the original version. It also features high-speed connections between chips, running at speeds that keep everything working smoothly together.

One of its biggest strengths is its scalability. Groups of Ironwood chips, called pods, can connect up to 9,216 units, sharing a massive 1.77 petabytes of memory to tackle the biggest AI jobs without getting stuck. This supports advanced AI techniques, from reinforcement learning to running AI on a global scale.

When stacked against Nvidia’s Blackwell chips, Ironwood holds its own in key areas. Google’s large-scale setups offer more memory and computing power, plus quicker links between chips, than Nvidia’s comparable systems like the GB300. It also edges out in energy use, consuming less power compared to Blackwell for similar high-precision AI tasks.

While Blackwell might handle more fine-tuned calculations, Ironwood’s focus on efficiency and massive scaling could make it a strong alternative for cloud-based AI operations.
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Source: https://247wallst.com/investing/2025/11/11/can-googles-ironwood-tpu-dethrone-nvidias-ai-empire/
Google is one to watch on AI. If I am reading the tea leaves correctly, it seems that these dedicated AI processors (TPUs or ASICs) will eventually overtake NVIDIA's GPUs. The same thing happened with Bitcoin, Ethereum and Monero. Which would be great news for gamers.

DataCamp said:
Comparisons between TPUs and GPUs in similar tasks frequently reveal TPUs outperforming GPUs in tasks specifically tailored to their architecture, providing quicker training durations and more effective processing.
For example, training a ResNet-50 model on the CIFAR-10 dataset for 10 epochs using an NVIDIA Tesla V100 GPU takes approximately 40 minutes, averaging 4 minutes per epoch. In contrast, using a Google Cloud TPU v3, the same training takes only 15 minutes, averaging 1.5 minutes per epoch.
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Source: https://www.datacamp.com/blog/tpu-vs-gpu-ai
Google's TPUs were already closing in on the performance of NVIDIA's GPUs a year ago?
Still, GPUs allow much more flexibility in programming potential - I believe TPUs are limited to TensorFlow and similar technologies.

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uOpt said:
Yawn. It's even marketed as being for inference.
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Gemini is trained on them, which is quite the proof of concept and meta/anthropic are opening the door to start training their models on them has well.

what is a bit funny with this news cycle, it was already all true when Gemini 2.5 launched, all on google stack, best model in the world, etc... the Google has always been really good at this since alpha Go and the launch of the TPU 10 years ago is not some news to people that follow this, that Ironwood is really good neither, everything said around google competition/gemini 3.0/etc... was all true with V6 TPU and Gemini 2.5 pro last june
 
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This has been one of the more interesting things to see recently. This suggests the market is predicting a bit of a move away from Nvidia and towards Google TPU. Probably will have that for certain applications, but Nvidia is still heavily protected in some areas by the dominance of CUDA.

Competition is good either way. Gemini 3 really validated Google’s TPUs as a viable option, as did Meta’s decision to buy a stack of them instead of the Nvidia competition.
 
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