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Our eighth generation TPUs: two chips for the agentic era

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Re: Our eighth generation TPUs: two chips for the agentic era

#151

Post by deaux »

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…

> If I had to guess the pro and flash variants are 5x to 10x smaller than opus and gpt-5 class models.

I really doubt it, especially Pro. If anything I wouldn't be surprised if their hardware lets them run bigger models more cheaply and quickly than the others. Pro is probably smaller than GPT 5.4 and Opus 4.6 (looks like 4.7 decreased in size), but 5x seems way too much. IMO Gemini 3 Pro is the most "intelligent" in an all-round human way. Especially in the humanities. It's highly knowledgeable and undeniably the number one model at producing natural text in a large number of (human!) languages. The difference becomes especially large for more niche languages. That does not suggest a smaller model, more the opposite. The top 4 models at multilinguality are all Google : 1. 3 Pro 2. 3 Flash 3. 2.5 Pro 4. 2.5 Flash. Even the biggest OpenAI and Anthropic models can't compete in that dimension.

It's definitely weaker at math and much worse at agentic things. Gemini chat as an app is also lightyears behind, it's barely different from ChatGPT at release over 3 yeaes ago. These things make it feel much weaker than it is.

Re: Our eighth generation TPUs: two chips for the agentic era

#152

Post by thatguysaguy »

geremiiah wrote: post #104

TPUs are systolic arrays right? So does that mean that Google is using a hetreogenous cluster compromising both GPUs and TPUs, for workloads that don't map well or at all on TPUs?

What sort of workloads are you thinking of?

Re: Our eighth generation TPUs: two chips for the agentic era

#153

Post by cmrdporcupine »

UncleOxidant wrote: post #107
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…

IIRC when Gemini 3 Pro came out it was considered to be just about on par with whatever version of Claude was out then (4?). Now Gemini 3 is looking long in the tooth. Considering how many Chinese models have been released since then, and at least 2 or 3 versions of Claude, it's starting to look like Google is kind of sitting still here. Maybe you're right and they'll surprise us soon with a large step improvement ov…

As other people are saying here: the Gemini models are mostly terrible at tool use and long context management. And maybe not quite as good with finicky "detail" parts of coding generally.

Where they excel is just total holistic _knowledge_ about the world. I don't like "talking" to it, because I kind of hate its tone, but I find Gemini generally extremely useful for research and analysis tasks and looking up information.

Re: Our eighth generation TPUs: two chips for the agentic era

#154

Post by sunaookami »

baq wrote: post #9
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.

you've never tried to use gemini 3 I guess - that thing was so unreliable it might as well not be offered; there's also a reason why everybody here is excited for claude and codex, but not really for antigravity. that said, I actually agree: google IMHO silently dominates the 'normie business' chatbot area. gemini is low key great for day to day stuff.

Yeah Gemini is not usable outside of the gemini.google.com homepage and AI Studio. Gemini CLI does not work at all and all the models are constantly overloaded or time out. Got a trial month and there was not a single day I could actually work with Gemini in a coding harness, 3.1 Pro was never reachable. Awful service, imagine if I'd have paid for this. Additionally since roughly two weeks Gemini 2.5 Flash (a stable GA model) is constantly throwing backend errors on Google's side when Grounding is on.

Re: Our eighth generation TPUs: two chips for the agentic era

#155

Post by [deleted] »

someguyiguess wrote: post #61
jmyeet wrote: post #56

In recent discussions about Tim Apple [sic] moving on there was a discussion about whether Apple flopped on AI, which is my opinion. Of course you had the false dichotomy of doing nothing or burning money faster than the US military like OpenAI does. IMHO that happy medium is Google. Not having to pay the NVidia tax will likely be a huge competitive advantage. And nobody builds data centers as cost-effectively as Goo…

Apple has not flopped on AI as you say. They are just focused on privacy and are likely waiting for the time when local models become efficient enough to run on iPhones (which is quickly becoming a reality). Google could probably train models for orders of magnitude less money as you say, but they aren't. They are not capable of creating high quality models like OpenAI and Anthropic are. Their company is just too dis…

[deleted]

Re: Our eighth generation TPUs: two chips for the agentic era

#156

Post by CuriouslyC »

cmrdporcupine wrote: post #153
UncleOxidant wrote: post #107

Earlier quoted context omitted.

IIRC when Gemini 3 Pro came out it was considered to be just about on par with whatever version of Claude was out then (4?). Now Gemini 3 is looking long in the tooth. Considering how many Chinese models have been released since then, and at least 2 or 3 versions of Claude, it's starting to look like Google is kind of sitting still here. Maybe you're right and they'll surprise us soon with a large step improvement ov…

As other people are saying here: the Gemini models are mostly terrible at tool use and long context management. And maybe not quite as good with finicky "detail" parts of coding generally. Where they excel is just total holistic _knowledge_ about the world. I don't like "talking" to it, because I kind of hate its tone, but I find Gemini generally extremely useful for research and analysis tasks and looking up informa…

Gemini had the best long context support for the longest time, and even now at >400k tokens it's still got the best long context recall.

Gemini is just not trained for autonomy/tool use/agentic behavior to the same degree as the other frontier models. Goog seems to emphasize video/images/scientific+world knowledge.

Re: Our eighth generation TPUs: two chips for the agentic era

#157

Post by cmrdporcupine »

CuriouslyC wrote: post #156
cmrdporcupine wrote: post #153

Earlier quoted context omitted.

As other people are saying here: the Gemini models are mostly terrible at tool use and long context management. And maybe not quite as good with finicky "detail" parts of coding generally. Where they excel is just total holistic _knowledge_ about the world. I don't like "talking" to it, because I kind of hate its tone, but I find Gemini generally extremely useful for research and analysis tasks and looking up informa…

Gemini had the best long context support for the longest time, and even now at >400k tokens it's still got the best long context recall. Gemini is just not trained for autonomy/tool use/agentic behavior to the same degree as the other frontier models. Goog seems to emphasize video/images/scientific+world knowledge.

My experience is it advertises large context and then just becomes incoherent and confused as it climbs to fill that context.

e.g. it sucks at general tool use but sucks even more at it after a chunk of time in a session. One frustrating situation is to watch it go into a loop trying and failing to edit source files.

I often wonder how my old coworkers from Google get by, if this is the the agentic coding they have available to them for working on projects on Google3. But I suspect the models they work with have been fine tuned on Google's custom tooling and perform better?

Re: Our eighth generation TPUs: two chips for the agentic era

#158

Post by dist-epoch »

mlmonkey wrote: post #113

FTA: > One pod of TPU 8t is 121 ExaFlops; or 121,000 PetaFlops. Meanwhile, the compute capacity of the top 10 supercomputers in the entire world is 11,487 Petaflops.[1] I know, I know, not the same flops, yada yada, but still. Just 1 pod alone is quite a beast. Edit: [1] https://top500.org/lists/top500/2025/11/

Not only not the same size, 4-bit flops versus 64-bit flops, but not the same programmability either. the TPUs can do just matrix-multiplications and some supporting math.

Otherwise bitcoin mining rigs dwarf everything, if you just want to count raw operations per second.

Re: Our eighth generation TPUs: two chips for the agentic era

#160

Post by dist-epoch »

nickandbro wrote: post #8

I am curious what workloads Citadel Securities is running on these TPUs? Are you telling me they need the latest TPUs for market insights?

Not Citadel, but Jane Street is training LLMs for trading:

https://www.janestreet.com/join-jane-street/machine-learning...

> We build on the latest papers in LLMs, computer vision, RL, training libraries, cuda kernels, or whatever else we need to train good models.

> We invent our own set of architectures and optimizations that work for trading.

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