OpenAI reportedly cut response costs for guest ChatGPT users by more than half
Quick Answer
OpenAI has reduced inference costs for guest ChatGPT users by over 50%, requiring only a few hundred Nvidia GPUs.
Quick Take
This optimization raises questions about its applicability to full-featured accounts, while Deepseek's new method promises a 60-85% speed increase in inference requests.
Key Points
- Inference costs for guest ChatGPT users cut by over 50%.
- Only a few hundred Nvidia GPUs are now needed for guest access.
- Deepseek introduced an open-source method speeding up inference by 60-85%.
- Optimizations may not apply to full-featured ChatGPT accounts.
- Data center buildouts are slow, impacting chip demand.
📖 Reader Mode
~1 min readOpenAI engineers told colleagues earlier this month that they'd managed to cut inference costs—the expense of running existing AI models—by more than half. That's according to a person familiar with the discussions, as reported by The Information.
OpenAI applied the new optimizations to ChatGPT, specifically for visitors who don't have an account. The number of Nvidia GPUs needed to serve those users dropped to just a few hundred. It's not clear how many were required before or what techniques OpenAI used to pull it off. Guest users can only access a very limited set of ChatGPT features, so whether these gains would carry over to the full product is an open question.
Deepseek also just dropped a new open-source method that can speed up inference requests by 60 to 85 percent. The freed-up resources could go toward scaling services, better models, faster responses, or bigger margins. But since data center buildouts are moving slowly, gains like these will probably give labs more breathing room rather than cut into chip demand.
— Originally published at the-decoder.com
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from The Decoder
See more →
An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run
Epoch AI's MirrorCode benchmark reveals Claude Opus 4.7 as the leader with a 56% solve rate, reconstructing a 16,000-line toolkit in 14 hours. Despite this, all models tested struggle with the most complex tasks, highlighting limitations in current AI capabilities. The single task consumed $2,600 over 19 days, raising questions about cost-effectiveness in AI development.
