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notable: they ripped out the silicon that supports training

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notable: they ripped out the silicon that supports training

they say: “it’s the age of inference”

which, yeah, RL is mostly inference. Continual learning is almost all inference. Ambient agents, fast growing inference demands in general audiences

kartik343.wixstudio.com/blogorithm/p...
Key Architecture Innovations
Ironwood's matrix multiply units (MXUs) have been entirely reengineered for inference-only operations with some major differences from training-centric architectures:
Sung Kim @sungkim.bsky.social
Google's 7th gen TPU Ironwood

10X peak performance improvement vs. TPU v5p, and more than 4X better performance per chip for both training + inference workloads vs. TPU v6e (Trillium).

They used TPUs to train + serve our own frontier models, including Gemini 3?

cloud.google.com/blog/product...
23 2
removing MXUs lets them reallocate transistor budget to inference tasks

they also dropped fp64 support — they’re all in on AI, it’s not going to be very useful for traditional supercomputing workloads
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