6 ms·

Few things to note: - Google qps is closer to 100k then 320k [1] - Not every query has to run on LLM. Probably only 10% would benefit from it - This means 10

Few things to note: - Google qps is closer to 100k then 320k [1] - Not every query has to run on LLM. Probably only 10% would benefit from it - This means 10,000 queries per second, each needing 5 A100s to run, so 50,000 A100s are sufficient. Cost for that is $500MM, quadruple that to $2B with CPU/RAM/storage/network. That is peanuts for Google. - Let's say AI unlocks new, never seen before queries ('do more people live in madrid or tel aviv') and those are 10x in volume. So capex is now $20B, still peanuts. - Latency, not cost, is a bigger issue. This should be addressed soon by H100 and newer chips. - The main issue for the consumer remains the business model. Who will pay for LLMs (user or the advertiser) and will Google and Microsoft stuff ads into LLM responses? [1] https://www.internetlivestats.com/one-second/#google-band https://www.internetlivestats.com/one-second/#google-band

jeffbee 4y ago
A service that gets 100kqps average can expect to get 200kqps at peak due to human diurnal cycle. Services also need regional and global failover capacity and generally would not be run right at the redline all the time, so you can figure in more slop factors. Now you are closer to 320k than to 100k. Also, the main factor in your analysis, 10%, was just completely made up by you.
- freediver 4y ago
  Fair points.
fnordpiglet 4y ago
I would expect specific hardware acceleration for LLM if it becomes very relevant. That would improve latency and costs. I use ChatGPT throughout the day as a widely knowledgeable coworker that periodically eats a bag of mushrooms, and have paid the $20 / month. I’d note this description matches all the most useful coworkers I’ve had, except that they cost a ton more per month.
- fnbr 4y ago
  What specific hardware acceleration is possible? I think TPUs are already as good as you can get for accelerating large MLPs (half of what a LLM is). Is there that much overhead for the attention blocks?
  - dylan522p 4y ago
    No TPUs are getting better architecturally
  - sinenomine 4y ago
    TPU is a pretty conservative design, there are 2x...5x speedups available with full-stack optimization, and then some.
- pavelstoev 4y ago
  Before spending $$$ on the hardware accelerators, there is still a lot to achieve using software optimizations for inference and trainings workloads. We optimized GPT-J, and others, for real. https://centml.ai/benchmarks/ https://centml.ai/benchmarks/
  - Const-me 4y ago
    You could also port that code from nVidia CUDA somewhere else. If you wanna run on servers, Vulkan Compute will probably do the trick. I have ported ML inference to Windows desktops with DirectCompute https://github.com/Const-me/Whisper https://github.com/Const-me/Whisper These NVIDIA A40 GPUs mentioned on the page you’ve linked cost $4000 (Amazon) to $10000 (Dell), deliver similar performance to Intel A770 which cost $350. That’s 1 order of magnitude difference in cost efficiency.
- omnicognate 4y ago
  Better to treat it as a devious and eloquent coworker commiting corporate sabotage for a competing company, who is paid by the factoid to get you to believe incorrect things. Google failed at this by being not nearly paranoid enough when putting their Bard demo together. Everyone laughed at them but ChatGPT does exactly the same thing. (Someone will probably claim ChatGPT would be less likely than Bard to commit the sort of fabrication spotted in the demo, but it's not. I gave ChatGPT the exact same question but replaced JWST with CERN and it told me antimatter was discovered at CERN when according to CERN's own website [1] it was both predicted and observed decades before CERN existed. I didn't have to fish for this. It was the first result for basically the exact same question and was a highly believable fabrication of the exact same kind, attributing a discovery that had been made earlier. I switched JWST for CERN just in case there has been any special training regarding JWST in light of the publicity - no idea if there has been, of course.) [1] https://timeline.web.cern.ch/timeline-header/86 https://timeline.web.cern.ch/timeline-header/86
schneems 4y ago
> The main issue for the consumer remains the business model. Who will pay for LLMs (user or the advertiser) I had a really weird idea the other day. What if the government invested in computer infra like it does for roads. Give each consumer X compute credits that they can use directly at cloud providers or trade in to services to help offset (or completely pay for) their own use. I would use it to run my open source infra. I imagine my parents would use it to decrease their Netflix bill (for example). I think it could unlock interesting non-marketing based “free” services. But it’s a hard concept to pitch to the average person who wouldn’t know why they would want 1000 CPU hours (for example).
- freediver 4y ago
  Government should be building this infra for the benefit of citizens, for vital services such as search. There should be a public search engine. Public libraries were good enough for that role but not anymore.
  - schneems 4y ago
    I think public competition for digital services would be amazing. I also think they should be a player, not take over the game. I think the answer would be for them to host a competing service AND allow people to use their credits to choose that service or a different vendor.
dylan522p 4y ago
- Google qps is closer to 100k then 320k [1] That number is wrong. I have a number from googler, not livestats which cannot have google internal data - Not every query has to run on LLM. Probably only 10% would benefit from it Agree, i have something different coming up that looks into this more, 10% may be too low. I know i used 100% which isn't right, and explicitly say that - This means 10,000 queries per second, each needing 5 A100s to run, so 50,000 A100s are sufficient. Cost for that is $500MM, quadruple that to $2B with CPU/RAM/storage/network. That is peanuts for Google. 50k A100s networking ramp cost way more than $2B HW utilization rate - Latency, not cost, is a bigger issue. This should be addressed soon by H100 and newer chips. Thats discussed in the subscriber section. It's both, but yes latency is bigger issue. H100 helps but doesn't solve.
echelon 4y ago
> Who will pay for LLMs (user or the advertiser) and will Google and Microsoft stuff ads into LLM responses? Capex: $20B Driving Google's margin to zero and feasting on their remains: priceless https://www.ft.com/content/2d48d982-80b2-49f3-8a83-f5afef98e8eb https://www.ft.com/content/2d48d982-80b2-49f3-8a83-f5afef98e... > “From now on, the [gross margin] of search is going to drop forever,” Nadella said in an interview with the Financial Times. > “There is such margin in search, which for us is incremental. For Google it’s not, they have to defend it all,” he added, referring to the competition against Google as “asymmetric”.
- code_biologist 4y ago
  The continued decay of search quality will be painful, as the economics make "good" consumer-oriented results uneconomical. I also fear whatever monetization approaches Google comes up with in search of margin. Corporations do really dumb things in service of monetization (see Hasbro / Wizards of the Coast with the D&D controversy of late).

jeffbee 4y ago

A service that gets 100kqps average can expect to get 200kqps at peak due to human diurnal cycle. Services also need regional and global failover capacity and generally would not be run right at the redline all the time, so you can figure in more slop factors. Now you are closer to 320k than to 100k. Also, the main factor in your analysis, 10%, was just completely made up by you.

freediver 4y ago
Fair points.

fnordpiglet 4y ago

I would expect specific hardware acceleration for LLM if it becomes very relevant. That would improve latency and costs. I use ChatGPT throughout the day as a widely knowledgeable coworker that periodically eats a bag of mushrooms, and have paid the $20 / month. I’d note this description matches all the most useful coworkers I’ve had, except that they cost a ton more per month.

fnbr 4y ago
What specific hardware acceleration is possible? I think TPUs are already as good as you can get for accelerating large MLPs (half of what a LLM is). Is there that much overhead for the attention blocks?
- dylan522p 4y ago
  No TPUs are getting better architecturally
- sinenomine 4y ago
  TPU is a pretty conservative design, there are 2x...5x speedups available with full-stack optimization, and then some.
pavelstoev 4y ago
Before spending $$$ on the hardware accelerators, there is still a lot to achieve using software optimizations for inference and trainings workloads. We optimized GPT-J, and others, for real. https://centml.ai/benchmarks/ https://centml.ai/benchmarks/
- Const-me 4y ago
  You could also port that code from nVidia CUDA somewhere else. If you wanna run on servers, Vulkan Compute will probably do the trick. I have ported ML inference to Windows desktops with DirectCompute https://github.com/Const-me/Whisper https://github.com/Const-me/Whisper These NVIDIA A40 GPUs mentioned on the page you’ve linked cost $4000 (Amazon) to $10000 (Dell), deliver similar performance to Intel A770 which cost $350. That’s 1 order of magnitude difference in cost efficiency.
omnicognate 4y ago
Better to treat it as a devious and eloquent coworker commiting corporate sabotage for a competing company, who is paid by the factoid to get you to believe incorrect things. Google failed at this by being not nearly paranoid enough when putting their Bard demo together. Everyone laughed at them but ChatGPT does exactly the same thing. (Someone will probably claim ChatGPT would be less likely than Bard to commit the sort of fabrication spotted in the demo, but it's not. I gave ChatGPT the exact same question but replaced JWST with CERN and it told me antimatter was discovered at CERN when according to CERN's own website [1] it was both predicted and observed decades before CERN existed. I didn't have to fish for this. It was the first result for basically the exact same question and was a highly believable fabrication of the exact same kind, attributing a discovery that had been made earlier. I switched JWST for CERN just in case there has been any special training regarding JWST in light of the publicity - no idea if there has been, of course.) [1] https://timeline.web.cern.ch/timeline-header/86 https://timeline.web.cern.ch/timeline-header/86

schneems 4y ago

> The main issue for the consumer remains the business model. Who will pay for LLMs (user or the advertiser) I had a really weird idea the other day. What if the government invested in computer infra like it does for roads. Give each consumer X compute credits that they can use directly at cloud providers or trade in to services to help offset (or completely pay for) their own use. I would use it to run my open source infra. I imagine my parents would use it to decrease their Netflix bill (for example). I think it could unlock interesting non-marketing based “free” services. But it’s a hard concept to pitch to the average person who wouldn’t know why they would want 1000 CPU hours (for example).

freediver 4y ago
Government should be building this infra for the benefit of citizens, for vital services such as search. There should be a public search engine. Public libraries were good enough for that role but not anymore.
- schneems 4y ago
  I think public competition for digital services would be amazing. I also think they should be a player, not take over the game. I think the answer would be for them to host a competing service AND allow people to use their credits to choose that service or a different vendor.

dylan522p 4y ago

- Google qps is closer to 100k then 320k [1] That number is wrong. I have a number from googler, not livestats which cannot have google internal data - Not every query has to run on LLM. Probably only 10% would benefit from it Agree, i have something different coming up that looks into this more, 10% may be too low. I know i used 100% which isn't right, and explicitly say that - This means 10,000 queries per second, each needing 5 A100s to run, so 50,000 A100s are sufficient. Cost for that is $500MM, quadruple that to $2B with CPU/RAM/storage/network. That is peanuts for Google. 50k A100s networking ramp cost way more than $2B HW utilization rate - Latency, not cost, is a bigger issue. This should be addressed soon by H100 and newer chips. Thats discussed in the subscriber section. It's both, but yes latency is bigger issue. H100 helps but doesn't solve.

echelon 4y ago

> Who will pay for LLMs (user or the advertiser) and will Google and Microsoft stuff ads into LLM responses? Capex: $20B Driving Google's margin to zero and feasting on their remains: priceless https://www.ft.com/content/2d48d982-80b2-49f3-8a83-f5afef98e8eb https://www.ft.com/content/2d48d982-80b2-49f3-8a83-f5afef98e... > “From now on, the [gross margin] of search is going to drop forever,” Nadella said in an interview with the Financial Times. > “There is such margin in search, which for us is incremental. For Google it’s not, they have to defend it all,” he added, referring to the competition against Google as “asymmetric”.

code_biologist 4y ago
The continued decay of search quality will be painful, as the economics make "good" consumer-oriented results uneconomical. I also fear whatever monetization approaches Google comes up with in search of margin. Corporations do really dumb things in service of monetization (see Hasbro / Wizards of the Coast with the D&D controversy of late).