61–70 of 213 posts
interesting that their domain is still engineering.fb.com
I think fb.com is their internal domain and they never really bothered to change it, I think employees used to have a @fb.com e-mail, at least this was true a few years ago, not sure if that has changed.
Earlier quoted context omitted.
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?
Earlier quoted context omitted.
and so is Meta: https://ai.meta.com/blog/next-generation-meta-training-infer...
Ever since Apple did it everyone has leaped on board. Let's see how things pan out for everyone...
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…
How will Meta leverage LLMs at scale to drive revenue? It's not clear.
Earlier quoted context omitted.
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?
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…
You mean the thing that's already stopped them? If they had seriously invested into the TPU ecosystem in 2015, they would already have "won" AI.
Earlier quoted context omitted.
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?
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…
Their work on folding and ai could very well be a business worth in the hundreds of billions and they know it as well as many other bets. Wheres others are playing the llm race to the bottom.
And we'll be writing case studies of how they squandered billions of R&D to help found other companies (kinda like Xerox Parc)
Almost every interesting paper after transformers has had it's authors leave to commercialize their own companies.