91–100 of 213 posts
Posts like this underscore why the smart money is betting on Google as the long term AI winner. Meta, Microsoft, OpenAI, etc. are trying to address problems with consumer video cards and spending billions to try and out bid each other to win Nvidia's favor - while Google is on their 6th generation of custom silicon. Literally the only thing that can stop Google now is the fact they keep bringing Microsoft and Oracle…
Posts like this underscore why the smart money is betting on Google as the long term AI winner. Meta, Microsoft, OpenAI, etc. are trying to address problems with consumer video cards and spending billions to try and out bid each other to win Nvidia's favor - while Google is on their 6th generation of custom silicon. Literally the only thing that can stop Google now is the fact they keep bringing Microsoft and Oracle…
Much the same way you can have all the best gear and still fail - Google’s primary strength seems to be the DeepMind group. I’m not affiliated with Google, but IMHO the reason they will slowly die is because their engineering culture has taken a backseat due to their broken hiring practices. Bad hiring practices aren’t exclusive to them, but from all accounts it seems like their internal focus is on optimizing ad rev…
What company do you think has better hiring practices, and subsequently a higher talent pool? Meta is pretty similar to Google's (though with an emphasis on speed over creativity). Microsoft is certainly worse at hiring than the two aforementioned...
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I think it's likely Nvidia's GPU's, many of which are $50,000+ for a single unit, far surpass Google's custom silicon otherwise why wouldn't Google be selling shovels like Nvidia? If Google had a better chip, or even a chip that was close, they would sell it to anyone and everyone. From a quick search I can see Google's custom chips are 15x to 30x slower to train AI compared to Nvidia's current latest gen AI specific…
We have almost 400 H100's sitting idle. I wonder how many other companies are buying millions of dollars worth of these chips with the hopes of them being used, but aren't being utilized?
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I think it's likely Nvidia's GPU's, many of which are $50,000+ for a single unit, far surpass Google's custom silicon otherwise why wouldn't Google be selling shovels like Nvidia? If Google had a better chip, or even a chip that was close, they would sell it to anyone and everyone. From a quick search I can see Google's custom chips are 15x to 30x slower to train AI compared to Nvidia's current latest gen AI specific…
We have almost 400 H100's sitting idle. I wonder how many other companies are buying millions of dollars worth of these chips with the hopes of them being used, but aren't being utilized?
jonathan [at] tensordock.com
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The only thing that can stop Google is Google. Somehow every bet that isn't Search doesn't pan out. And inexplicably, they're working hard to kill Search now. As a shareholder, I hope they succeed. But I am more pessimistic about it than you.
And they missed multiple waves of effectively building on their own in house research.
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Ever since Apple did it everyone has leaped on board. Let's see how things pan out for everyone...
And that's why $ARM is a good buy. Selling swords and steel to all these armies as they go to war.
I'm not saying it's a bad buy, either, but if and when they turn the screws, there will be a mass exodus. Solid long term play perhaps, but not going to see Nvidia like price action.
Posts like this underscore why the smart money is betting on Google as the long term AI winner. Meta, Microsoft, OpenAI, etc. are trying to address problems with consumer video cards and spending billions to try and out bid each other to win Nvidia's favor - while Google is on their 6th generation of custom silicon. Literally the only thing that can stop Google now is the fact they keep bringing Microsoft and Oracle…
I think it's likely Nvidia's GPU's, many of which are $50,000+ for a single unit, far surpass Google's custom silicon otherwise why wouldn't Google be selling shovels like Nvidia? If Google had a better chip, or even a chip that was close, they would sell it to anyone and everyone. From a quick search I can see Google's custom chips are 15x to 30x slower to train AI compared to Nvidia's current latest gen AI specific…
[1] https://mlcommons.org/benchmarks/training/
[2] https://cloud.google.com/blog/products/compute/introducing-t...
Posts like this underscore why the smart money is betting on Google as the long term AI winner. Meta, Microsoft, OpenAI, etc. are trying to address problems with consumer video cards and spending billions to try and out bid each other to win Nvidia's favor - while Google is on their 6th generation of custom silicon. Literally the only thing that can stop Google now is the fact they keep bringing Microsoft and Oracle…
Earlier quoted context omitted.
Ever since Apple did it everyone has leaped on board. Let's see how things pan out for everyone...
Google introduced their first TPU in 2015...? Long before Apple taped out their first silicon.
Posts like this underscore why the smart money is betting on Google as the long term AI winner. Meta, Microsoft, OpenAI, etc. are trying to address problems with consumer video cards and spending billions to try and out bid each other to win Nvidia's favor - while Google is on their 6th generation of custom silicon. Literally the only thing that can stop Google now is the fact they keep bringing Microsoft and Oracle…
I think it's likely Nvidia's GPU's, many of which are $50,000+ for a single unit, far surpass Google's custom silicon otherwise why wouldn't Google be selling shovels like Nvidia? If Google had a better chip, or even a chip that was close, they would sell it to anyone and everyone. From a quick search I can see Google's custom chips are 15x to 30x slower to train AI compared to Nvidia's current latest gen AI specific…
They do sell them - but through their struggling cloud business. Either way, Nvidia's margin is google's opportunity to lower costs.
> I can see Google's custom chips are 15x to 30x slower to train AI
TPUs are designed for inference not training - they're betting that they can serve models to the world at a lower cost structure than their competition. The compute required for inference to serve their billions of customers is far greater than training costs for models - even LLMs. They've been running model inference as a part of production traffic for years.