OpenAI has temporarily stopped selling the Plus plan
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Plus, you can always use Bing Chat. It’s more locked down, but depending on what you’re doing, it’s pretty good and backed by GPT-4.
As for Bing, despite the claims that it is GPT-4, it's clearly inferior to what OpenAI is offering. It's probably an older iteration of the model, and I wonder if it might also be a scaled-down version to run it cheaper at scale.
As for Bing? Who knows what's going on behind the scenes. I've read that the "Creative" mode uses GPT-4 while the others might use a faster model, but that may well be nonsense.
As for Bing, I don't know what's behind the scenes, but it does noticeably worse on tasks in "Creative" mode than ChatGPT backend in GPT-4 mode, in my limited experiments. For example, try this:
> A is 1m left of B, B is 1m above C, D is 1m right of C, E is 1m below D, and E is 1m right of F. Where is F located relative to C?
GPT-4 can usually solve this correctly. Bing is usually wrong even when it tries to solve it step by step (and it often won't unless you prompt it).
How?
I would wager against comment 0 that said that this will materially affect earnings.
People here wanted Google scale of operations and replacing Google with ChatGPT as their search engine.
But it seems that some here have forgotten about the high availability requirements and incalculable operational costs of running a search engine. Google.com's availability is a very high barrier of serving in the multi-billions of searches a day.
Azure has lots of experience in unreliability and running services at a massive loss whilst minting credits to compete on cost.
What is the true marginal cost to MSFT for gpt workloads anyway? As long as it's not bumping paying customers then it's what the cost of electricity?
And save a ton of money at the same time.
I have found Bing and Bard a lot more efficient for tasks where you need to look stuff up on the web, probably because they both have local access to a crawler index. They do tend to need more guidance though.
I'm sure serious competitors are just around the corner, at least Bard will catch up, No doubt Apple has something in the works, there's little to zero doubt about that.
So do they make a huge capital investment hoping that growth will continue at this trajectory, or do they back off the gas pedal ?
I bet there's also some contentious ideas going on internally too. If they go on a huge hiring spree, does that kind of invalidate the product which is supposed to replace everyone? How do the optics look there? Do they hire and fire once GPT-5 becomes a sentient AGI?
Google already has a huge engineering team, operates are unprecedented scale, and IMO has a much more diverse set of products to integrate with, they also solve a lot of novel solutions which they require specialized engineers that can't be replaced easily by "AI".
Be interesting to see how it plays out. Maybe a victim of their own success ?
Once everyone has their prompts and embeddings tuned and is serving customers, OpenAI can dial up the price and customers chose to keep paying or redo their prompts/embeddings to switch to Bard or the next competitor. It does seem like business as usual if you relied on GPT-3 now ChatGPT, then whatever iteration comes next, probably won't work the same, so its not a heavy heavy lift. Decent platform risk, I believe OpenAI shutdown Codex so now everyone who had a product based on Codex has to switch to ChatGPT
commit author: GPT-4
commit message: ":rocket_emoji: Set all Azure clusters to autoscale in order to resolve flooded message queue"
It is also like accepting the hilarious availability times of GitHub and then saying 'GitHub is doing their best' whilst they have an outage every week.
Lets just say that a service that claims to scale to more than 50 million or 100 million users going down each week is not acceptable or being an apologist for large companies unable to scale with the so-called best engineers working for them.
> I bought it and it was removed off of my account. I paid money for a feature I never got. Anyone else get this?
It look like a very bad experience. if i were him, i would issue a credit card charge back
Let's just look at GPT 3. Each forward pass requires 2N=350B flops per token. The computational overhead of the attention mechanism is negligible but the memory overhead of the attention for all users is not, but we can ignore that for now. Let's assume each query involves ~200 tokens of combined input and output. That's 70T flops per query. Let's say the cost of compute is 1 cent per 100B flops (probably a lowball). That's $7 per query for compute alone.
And that's just GPT 3.
With sparse matrix and 8-bit precision, you can effectively get over 100x the performance on an A100 than with FP64, plus the memory requirements are more than an order of magnitude less. Factor of 128 improvement in processing speed plus at least a factor of 8-16 or more improvement in tokens per memory, depending on sparsity. (Sparsity and reduced precision do reduce performance for the same number of operations and weights, but the overall effect on performance from reducing precision is beneficial probably at least to 4-bit quantization… You get diminishing or negative returns if you go less than that with current architectures, but in principle we can keep going.)
https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Cent...
I think your compute cost figure is both very old and probably assumes double precision whereas GPT-3 uses at most FP16 at inference and GPT-4 may go beyond that.
> Let's say the cost of compute is 1 cent per 100B flops (probably a lowball)
That's like 0.5ms worth of compute on A100. VMs with an A100 cost a few dollars per hour at cloud providers, i.e. about a cent per second or 0.001 cents per ms. OpenAI would obviously get a far better deal than somebody renting a single GPU for an hour.
Look, we know what OpenAI is charging for their commercial service. We don't know how it maps to their actual serving costs; maybe they're breaking even, making a small profit, or making a small loss. But just basic common sense should tell you that they are not operating at -10000% gross margins.
I suspect that just working backwards from the prices to a reasonable range of gross margins is going to produce much better estimates than your methodology would even after the inputs are tuned to be more realistic.
I sourced my cost/flop estimate from here: https://aiimpacts.org/2019-recent-trends-in-gpu-price-per-fl...
I'll acknowledge that a fleet entirely made up of A100 GPUs running at full capacity 100% of the time with Azure's 3-year commitment pricing (likely very low margin) would make this 5 orders of magnitude cheaper. But the sequential nature of transformers makes it difficult to achieve perfect utilization, and there aren't enough A100 GPUs in the Azure fleet to serve ChatGPT's billion users, so they're certainly running on mostly lesser hardware. Also, my estimate was for GPT-3 not 4, and neglected to address any memory or bandwidth costs, not to mention the costs to train and iterate on the models in the first place, including legions of data curators and many researchers commanding million dollar salaries.
If ChatGPT plus is $20/mo and each user uses it ~10 times per day, then MS/OpenAI would break even if my estimate were 2 orders of magnitude too high. Within the year, we'll have a much better idea of the real cost of these models.
The other thing to note is that OpenAI limits paying users to X queries per Y hours in GPT-4, something like 3 per 5. Which means the GPT-4 fleet is quite limited.
It also keeps saying "expect significantly lower caps, as we adjust for demand", but it's been three weeks now and they haven't lowered it yet.
> I'll acknowledge that a fleet entirely made up of A100 GPUs running at full capacity 100% of the time with Azure's 3-year commitment pricing (likely very low margin) would make this 5 orders of magnitude cheaper. But the sequential nature of transformers makes it difficult to achieve perfect utilization,
I don't think you've thought this through. There's no realistic level of utilization that's going to make up for 5 orders of magnitude. An unimaginably crappy 1% utilization would still leave you off by 3 orders of magnitude.
This very submission about OpenAI having to suspend selling subscriptions makes it pretty clear they're not having trouble with low utilization...
> If ChatGPT plus is $20/mo and each user uses it ~10 times per day, then MS/OpenAI would break even if my estimate were 2 orders of magnitude too high.
You do understand that this doesn't actually support your initial estimate, right? It just makes it more obvious how ridiculous it was.
Also:
> Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something.
https://news.ycombinator.com/newsguidelines.html
If you're going to accuse me of using ChatGPT, at least bother to say why.
>> ... suppose you're describing this to a technical audience - software engineers, specifically. There might be some way to talk about percentages concisely in that particular context.
> In that case, you could describe the situation to a technical audience by saying, "The company's profit margin is effectively -∞% due to offering the product for free while incurring expenses."
I’ll be honest, I laughed out loud at this.
Pretty sure I read somewhere that Bing had around 1 billion searches per day BEFORE the hype of bingchat. At $7/query that makes MSFT’s $10 billion investment basically peanuts. Less than 2 days of Bings GPT usage payments. Lmao.
If they charged 1 cent per 100B flops that pricing would be $35.000 / 1K tokens. They may be taking a loss, but I doubt it would be at a 1 to 17,500 ratio.
Even GPT-4 with 32k token window is only $0.12 / 1K tokens
I've heard from insiders that the cost is roughly a couple cents per ChatGPT message but that was back with the legacy GPT-3.5 model, not the Turbo model or GPT-4
I might be totally misunderstanding this, but from the source you posted elsewhere[1], GPU Sale Price per Flops is as a little bit higher than 1*10^-11 dollars per flop, or 10^11 flops per dollar, or 10^9 flops per cent. So 1GFlops per cent. But Flops are Floating point operations per second. So you aren't paying 1cent for 100B flops, you're paying 1 cent for a card capable of doing 100B floating point operations per second. So let's say the card lasts 3 years and runs at 100% utilization (I know, it doesn't we can multiply by some efficiency metric later), there's about 9.5*10^7 seconds in 3 years. So that dollar cost isn't for 100B flops, it's for 100B * 9.5 *10^7 ( *some efficiency number). So it's many orders of magnitude cheaper than you're saying.
In reality, the cost of running these things isn't the cost of buying the GPUs, it's the cost of the power to run the GPUs, and that's a totally different calculation we can do.
[1]: https://aiimpacts.org/2019-recent-trends-in-gpu-price-per-fl...*