Google Unveils Two New AI Chips For the 'Agentic Era' (cnbc.com)
- Reference: 0181865364
- News link: https://tech.slashdot.org/story/26/04/22/1746252/google-unveils-two-new-ai-chips-for-the-agentic-era
- Source link: https://www.cnbc.com/2026/04/22/google-launches-training-and-inference-tpus-in-latest-shot-at-nvidia.html
> After years of producing chips that can both train artificial intelligence models and handle inference work, Google is separating those tasks into distinct processors, its latest effort to take on Nvidia in AI hardware. [...] None of the tech giants are displacing Nvidia, and Google isn't even comparing the performance of its new chips with those from the AI chip leader. Google did say the training chip enables 2.8 times the performance of the seventh-generation Ironwood TPU, announced in November, for the same price, while performance is 80% better for the inference processor.
>
> Nvidia said its upcoming Groq 3 LPU hardware will draw on large quantities of static random-access memory, or SRAM, which is used by Cerebras, an AI chipmaker that filed to go public earlier this month. Google's new inference chip, dubbed TPU 8i, also relies on SRAM. Each chip contains 384 megabytes of SRAM, triple the amount in Ironwood. The architecture is designed "to deliver the massive throughput and low latency needed to concurrently run millions of agents cost-effectively," Sundar Pichai, CEO of Google parent Alphabet, wrote in a blog post.
[1] https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/eighth-generation-tpu-agentic-era/
[2] https://www.cnbc.com/2026/04/22/google-launches-training-and-inference-tpus-in-latest-shot-at-nvidia.html
Apple is kinda replacing Nvidia ... (Score:3, Interesting)
Apple is kinda replacing Nvidia, not is the general market but within its product line. Its integrated GPUs and NPUs (neural processing unit) are designed for client-side AI processing. Accelerating ML models. It's all deeply interested into Apple Silicon CPUs, which are ARM64 based.
As for creating the ML models, I am not sure if Apple is competitive there. At the individual user level, with respect to Nvidia more affordable products.
Re: (Score:2)
Apple used to embed AMD and Nvidia GPUs in some high end products. Its no longer necessary.
Re: (Score:3)
Apple doesn't have devices with enough RAM to challenge Nvidia.
Apple also has no credibility in servers, after they got into them, then left, then got into them again, then left again. Nobody wants to be rugpulled.
Re: (Score:2)
Nvidia (and AMD too) doesn't care about retail anymore. For the first time, retail can not upgrade their hardware while older hardware are becoming more expensive. The steam machine, if it is released, will probably be one of the most common hardware. A machine those specs barely reach PS5 level. In normal times, having hardware reaching descent FPS for path tracing would have been the norm in 2027 because of Ai it will remain a privilege of high specs. Fortunately Apple is not into the AI server/research
Re: (Score:2)
> Apple doesn't have devices with enough RAM to challenge Nvidia.
Again, I am referring to the local ML model execution, acceleration. Apple Watches have done impressive on-board processing using ML models.
> also has no credibility in servers, ...
Not what I referred to.
Re: (Score:2)
The vast majority of LLM processing is done in the cloud and any AMD laptop has the functionality to run LLMs, plus probably expandable memory so if you can afford the RAM, you can run larger models than with Apple. Nobody cares yet. Maybe eventually.
Re: (Score:2)
Apple Silicon can't realistically "replace" a discrete. Rather, they're... different.
The compute performance of Apple Silicon is vastly inferior to a mid-range discrete. Its bandwidth isn't great in comparison, either.
So, in terms of GB-of-VRAM-to-GB-of-VRAM, Apple Silicon is worst than any discrete you're likely to have for ML purposes.
However, they've got something you can't get on a discrete- 128GB of VRAM in a laptop, and 512GB of VRAM in a desktop.
This changes the equation, because it means your Ap
NVidia + Google + Cerebras moving to SRAM (Score:3)
SRAM has never been built at this scale, afaik. Cerebras was ahead of the curve here, building wafer scale SRAMs years ago. The penalties of DRAM (even with HBM) are now so severe that everyone is taking the gloves off and building mighty SRAMs. This has always been possible in theory, but the high cost never justified it.
The impact on semiconductor fab demand is significant. SRAM cells are larger than DRAM bits: more silicon die area for the same amount of RAM.
Also, the training vs. inference split Google is baking into actual hardware is a big deal: it's the reality that training and inference are very distinct things asserting itself, which has been obvious to anyone that hasn't been drinking excessive NVidia cool-aid: there is a future where costly, general purpose GPU-like devices aren't actually necessary for operating LLMs.
Just wait... (Score:2)
Just wait until the bubble bursts, and everyone starts removing anything 'AI' from their devices (as much as possible), and stops using it because they're sick of the hallucinations or how it's baked-into everything... I'll keep using my Galaxy S9 (and Win10 Enterprise LTSC, and not-smart 18 year old Plasma TV) until it totally dies (only used Bixby like 5 times... mostly just seeing if it's worth using).
I don't need "Clod" to generate my Arduino code for me... I'll look up stuff on my own. I can type out
Re: (Score:2, Interesting)
> ah, not available, except to rent a time slice? fuck off, then.
In the context of building ML models, renting a virtual farm, may be the better solution. You think think the GPU upgrade cycle is bad, wait until you try to keep up with AI level products. :-)
Accelerating the ML model on your PC or Mac is a very different thing.