Our eighth generation TPUs: two chips for the agentic era

Post by ValentineC » Wed, Apr 22, 2026, 2:51 PM UTC

Keyframe wrote: post #4

As others have been capturing news cycle eyes, seems to me Google has been going from strength to strength quietly in the background capturing consumer market share and without much (any?) infrastructure problems considering they're so vertically integrated in AI since day one? At one point they even seemed like a lost cause, but they're like a tide.. just growing all around.

Post by asah » Wed, Apr 22, 2026, 3:01 PM UTC

onlyrealcuzzo wrote: post #71

himata4113 wrote: post #64

I already felt that gemini 3 proved what is possible if you train a model for efficiency. If I had to guess the pro and flash variants are 5x to 10x smaller than opus and gpt-5 class models. They produce drastically lower amount of tokens to solve a problem, but they haven't seem to have put enough effort into refinining their reasoning and execution as they produce broken toolcalls and generally struggle with 'agent…

> They produce drastically lower amount of tokens to solve a problem, but they haven't seem to have put enough effort into refinining their reasoning and execution as they produce broken toolcalls and generally struggle with 'agentic' tasks, but for raw problem solving without tools or search they match opus and gpt while presumably being a fraction of the size. Agreed, Gemini-cli is terrible compared to CC and even…

Post by ativzzz » Wed, Apr 22, 2026, 3:06 PM UTC

youniverse wrote: post #15

Keyframe wrote: post #4

As others have been capturing news cycle eyes, seems to me Google has been going from strength to strength quietly in the background capturing consumer market share and without much (any?) infrastructure problems considering they're so vertically integrated in AI since day one? At one point they even seemed like a lost cause, but they're like a tide.. just growing all around.

Yeah I think there will be a time in a few years (1-2?) when both Google and Apple will get to eat their cake. They aren't playing the same game of speed running unpolished product releases every month to double their valuation. They have time to think and observe and put out something really polished. At least that's the hope! :)

Post by lugu » Wed, Apr 22, 2026, 3:09 PM UTC

cyanydeez wrote: post #57

TheMrZZ wrote: post #2

> A single TPU 8t superpod now scales to 9,600 chips and two petabytes of shared high bandwidth memory, with double the interchip bandwidth of the previous generation. This architecture delivers 121 ExaFlops of compute and allows the most complex models to leverage a single, massive pool of memory. This seems impressive. I don't know much about the space, so maybe it's not actually that great, but from my POV it look…

it is. itll still not create AGI without some breakthrough in instruction vs data separation of concerns

Post by _boffin_ » Wed, Apr 22, 2026, 3:13 PM UTC

ALLTaken wrote: post #72

himata4113 wrote: post #64

I already felt that gemini 3 proved what is possible if you train a model for efficiency. If I had to guess the pro and flash variants are 5x to 10x smaller than opus and gpt-5 class models. They produce drastically lower amount of tokens to solve a problem, but they haven't seem to have put enough effort into refinining their reasoning and execution as they produce broken toolcalls and generally struggle with 'agent…

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