يُصمم شيب نابير من تينسوردنايب لتسريع استدلال IA. منذ حوالي 2020، ركز IA على تدريب نماذج أكبر وأفضل. زادت النماذج linguistic الكبيرة (LLMs) من ملايين المعاملات إلى تريليونات. أثبت هذا فعالية: النموذج الأكبر لـ GPT-3 من Open AI، الذي أطلق في 2020، أجاب بشكل صحيح فقط على 43.9% من الأسئلة في معيار شائع للمعرفة والتحليل. بعد أربع سنوات فقط، بلغ GPT-4o درجة 88.7% في نفس الاختبار، مطابقاً فعلياً لخبراء البشر. لا تزال مختبرات IA المتقدمة تدرب نماذج أكبر، لكن هذا التدريب تراجع قليلاً إلى خلفية حوار IA. في 2026، الاستدلال—which uses trained models to produce code, write essays, or make images of ourselves as elves—has come to the forefront.

“It's like training is yesterday's news,” says Matt Kimball, principal data-center analyst at Moor Insights & Strategy. “All that any chief information officer wants to talk about is inference.” Nvidia CEO Jensen Huang, speaking at the company's GTC 2026 conference, touted this change as the "inflection point of inference."

Part of what's caused the shift is very simple: LLMs are becoming useful, so people are using them. On top of that, many models on the market today are reasoning models. In response to a user's query, they run inference not just once but multiple times, reprompting themselves in a process called chain of thought. Reasoning models generate longer outputs, and models with high reasoning effort can produce up to 20 times as much text as those with low or no effort.

The resulting explosion in inference demand has led to unexpected alliances among tech giants. Open AI and Amazon have deployed chips the size of a dinner plate designed by Cerebras, despite Amazon having its own Trainium chips. Nvidia bought key talent and intellectual property from AI-inference startup Groq in a controversial deal worth US $20 billion. And Anthropic is paying LLM competitor Space XAI over a billion dollars per month to lease spare compute.