Comment by piyh
7 days ago
You have thousands of dollars, they have tens of billions. $1,000 vs $10,000,000,000. They have 7 more zeros than you, which is one less zero than the scale difference in users: 1 user (you) vs 700,000,000 users (openai). They managed to squeak out at least one or two zeros worth of efficiency at scale vs what you're doing.
Also, you CAN run local models that are as good as GPT 4 was on launch on a macbook with 24 gigs of ram.
https://artificialanalysis.ai/?models=gpt-oss-20b%2Cgemma-3-...
You can knock off a zero or two just by time shifting the 700 million distinct users across a day/week and account for the mere minutes of compute time they will actually use in each interaction. So they might no see peaks higher than 10 million active inference session at the same time.
Conversely, you can't do the same thing as a self hosted user, you can't really bank your idle compute for a week and consume it all in a single serving, hence the much more expensive local hardware to reach the peak generation rate you need.
During times of high utilization, how do they handle more requests than they have hardware? Is the software granular enough that they can round robin the hardware per token generated? UserA token, then UserB, then UserC, back to UserA? Or is it more likely that everyone goes into a big FIFO processing the entire request before switching to the next user?
I assume the former has massive overhead, but maybe it is worthwhile to keep responsiveness up for everyone.
Inference is essentially a very complex matrix algorithm run repeatedly on itself, each time the input matrix (context window) is shifted and the new generated tokens appended to the end. So, it's easy to multiplex all active sessions over limited hardware, a typical server can hold hundreds of thousands of active contexts in the main system ram, each less than 500KB and ferry them to the GPU nearly instantaneously as required.
2 replies →
This is great product design at its finest.
First of all, they never “handle more requests than they have hardware.” That’s impossible (at least as I’m reading it).
The vast majority of usage is via their web app (and free accounts, at that). The web app defaults to “auto” selecting a model. The algorithm for that selection is hidden information.
As load peaks, they can divert requests to different levels of hardware and less resource hungry models.
Only a very small minority of requests actually specify the model to use.
There are a hundred similar product design hacks they can use to mitigate load. But this seems like the easiest one to implement.
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During peaks they can kick out background jobs like model training or API users doing batch jobs.
In addition to stuff like that they also handle it with rate limits, that message that Claude would throw almost all the time when they were like "demand is high so you have automatically switched to concise mode", making batch inference cheaper for API customers to convince them to use that instead of real time replies. The site erroring out during a period of high demand also works, prioritizing business customers during a rollout, the service degrading. It's not like any provider has a track record for effortlessly keeping responsiveness super high. Usually it's more the opposite.