Sam Altman says OpenAI was forced to stagger GPT-4.5's rollout because it is “out of GPUs”; the model is wildly expensive, costing $75 per million input tokens
Hopefully the output is worth it? 🤔 — Oh... 😥 — www.theverge.com/news/620021/ ... [embedded post] X: Ed Zitron / @edzitron : Also, $1.30 per hour per GPU is the Microsoft discount rate for OpenAI. Safe to assume there are other costs but raw compute for GPT 4.5 is massive and committing such resources at this time is truly fatalistic. suggests Altman has no other cards to play https://www.theinformation.com/ ... Robert Scoble / @scobleizer : OpenAI basically said the same thing too. It doesn't have enough NVIDIA. Of course its stock went down 8.5% today in reaction. Ed Zitron / @edzitron : Sam Altman is talking about bringing online “tens of thousands” and then “Hundreds of thousands” of GPUs. 10,000 GPUs costs them $113 million a year, 100k $1.13bn, so this is Sam Altman committing to billions of dollars to an expensive model that lacks any real new use cases. [image] Jeremy Howard / @jeremyphoward : # The *actual* LLM scaling law. Adding more compute and data to LLMs makes them: - Linearly more expensive, and - Logarithmically more useful. Therefore: - Scaling becomes less useful the more you do it (once you reach a point where cost is non-trivial). Casper Hansen / @casper_hansen_ : GPT 4.5 pricing is unhinged. If this doesn't have enormous models smell, I will be disappointed [image] Bindu Reddy / @bindureddy : TBH, we should thank OpenAI for dropping the API even when they are GPU constrained THANKS, OAI! 🙏🙏 Still don't have Grok 3 🤷 Farzad / @farzyness : Why didn't OpenAI wait to get 4.5 down to reasonable pricing before they showed incremental improvement? My guess is because they need to stay relevant in public discourse + with investors due to competition models being just as good, if not better. Ina Fried / @inafried : Some details in this post from @sama including a big GPU influx needed - and coming - to serve GPT 4.5 Forums: r/NvidiaStock : OpenAI CEO Sam Altman says the company is ‘out of GPUs’ | TechCrunch r/singularity : OpenAI CEO Sam Altman says the company is ‘out of GPUs’ r/nvidia : OpenAI CEO Sam Altman says the company is ‘out of GPUs’ | TechCrunch r/technology : OpenAI CEO Sam Altman says the company is ‘out of GPUs’ | TechCrunch BeauHD / Slashdot : OpenAI Sam Altman Says the Company Is ‘Out of GPUs’ Msmash / Slashdot : OpenAI Rolls Out GPT-4.5
Context & Ripple Effects
OpenAI had already signaled that compute allocation was constraining its roadmap: Altman said the company faced hard decisions in allocating compute, while reporting on GPT-5 described high computing costs and technical hurdles. GPT-4.5 makes that constraint visible at deployment rather than only in future-model planning.
The reported rollout limits also frame GPT-4.5’s positioning. Coverage of the model found it could match or beat GPT-4o on some coding benchmarks but did not clearly surpass every OpenAI alternative, sharpening the question of whether its additional serving cost is justified.
First-order effects
- GPT-4.5 availability must be staggered while OpenAI lacks sufficient GPU capacity, limiting how quickly users and API customers can be served.
- OpenAI must operate a model reported to cost $75 per million input tokens, making each increment of usage materially more expensive to support.
Second-order effects
- OpenAI’s GPU fleet must be allocated between GPT-4.5 and other products or model work, extending the compute-allocation trade-offs it had already acknowledged.
- Customers with workloads that do not require GPT-4.5 may have a stronger incentive to use lower-cost OpenAI models, while rivals can compete on availability and serving economics.
Third-order effects
- If advanced models remain expensive to serve, inference capacity—not just model quality—will increasingly determine which launches can scale broadly.
- The pattern favors AI providers able to secure and efficiently use large GPU fleets, potentially making infrastructure access a more durable competitive divider.
The trend: Frontier AI is shifting from a race to train ever-larger models toward a race to finance, secure, and efficiently operate the inference capacity needed to serve them.