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Does the article has anything to say apart from clickbait? From article, Google is working on a 3nm chip with TSMC, so it is surely not abandoned. Current chip

Does the article has anything to say apart from clickbait? From article, Google is working on a 3nm chip with TSMC, so it is surely not abandoned. Current chip sometimes run hot, that's bad but not an existential crisis. Not all AI things can be on-device, was that ever a promise? Is that even feasible? Is any competitor doing better? LLMs with 100s of billions of params can't be run on a beefy desktop, so why is it a surprise or concern that bard can't run on Pixel.

Roark66 3y ago
I came here to say exactly the same. Where is any substance in this article? What are the capabilities of the chips? How many Tflops can they do? I'm not buying it. I have a couple of Google's (mini) Edge TPUs doing object recognition in my Cctv server. They have been working 24/ 4 for about 2 years and are consistently delivering on the 4W/4Tflops promise(although for some reason they stopped advertising this number focusing on benchmarks instead). I for one wish Google would have us their big (or even medium) sized TPUs to play with on an add on card rather than in their phone.
- brucethemoose2 3y ago
  > I for one wish Google would have us their big (or even medium) sized TPUs to play with on an add on card rather than in their phone. Yeah, no kidding. I know they want to push GCS, but what a boon that would be for TPU support in ML projects.
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dacryn 3y ago
headlines of google products being end of life and perception of being killed off generates a lot of clicks, and it is so tiring
gorbypark 3y ago
While intentionally a bit click-baitey, I think the author is saying that features _using_ the ML tensor cores are on life support. They announced/hinted at that a bunch of features would use on-device ML and in the end they are just using the cloud like they always have.
- microtonal 3y ago
  As far as I understand, some inference is done on-device. LLMs and diffusion just changed the field in the last year and it takes time for hardware to catch up (+ work to reduce model sizes). So, it's just hard to run the latest models on-device. So you either end up doing it online (Google's preference) or having weaker models (Apple's preference).

Roark66 3y ago

I came here to say exactly the same. Where is any substance in this article? What are the capabilities of the chips? How many Tflops can they do? I'm not buying it. I have a couple of Google's (mini) Edge TPUs doing object recognition in my Cctv server. They have been working 24/ 4 for about 2 years and are consistently delivering on the 4W/4Tflops promise(although for some reason they stopped advertising this number focusing on benchmarks instead). I for one wish Google would have us their big (or even medium) sized TPUs to play with on an add on card rather than in their phone.

brucethemoose2 3y ago
> I for one wish Google would have us their big (or even medium) sized TPUs to play with on an add on card rather than in their phone. Yeah, no kidding. I know they want to push GCS, but what a boon that would be for TPU support in ML projects.

deleted 3y ago

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dacryn 3y ago

headlines of google products being end of life and perception of being killed off generates a lot of clicks, and it is so tiring

gorbypark 3y ago

While intentionally a bit click-baitey, I think the author is saying that features _using_ the ML tensor cores are on life support. They announced/hinted at that a bunch of features would use on-device ML and in the end they are just using the cloud like they always have.

microtonal 3y ago
As far as I understand, some inference is done on-device. LLMs and diffusion just changed the field in the last year and it takes time for hardware to catch up (+ work to reduce model sizes). So, it's just hard to run the latest models on-device. So you either end up doing it online (Google's preference) or having weaker models (Apple's preference).