OpenAI reportedly cut inference costs for guest ChatGPT users by more than half, reducing GPU requirements to a few hundred, though it's unclear if these optimizations apply to the full product.
Guest users can only access a limited set of features, so it is unclear whether the performance gains will carry over to the full product.
# OpenAI reportedly cut response costs for guest ChatGPT users by more than half
Source: [https://the-decoder.com/openai-reportedly-cut-response-costs-for-guest-chatgpt-users-by-more-than-half/](https://the-decoder.com/openai-reportedly-cut-response-costs-for-guest-chatgpt-users-by-more-than-half/)
**OpenAI 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](https://www.theinformation.com/newsletters/ai-agenda/openai-discovers-new-way-cut-inference-costs-half)\.
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](https://the-decoder.de/deepseeks-dspark-beschleunigt-ki-antworten-pro-nutzer-um-bis-zu-85-prozent/)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\.
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