Report: OpenAI Halves AI Model Inference Costs Through System-Level Optimization

On June 30, reports emerged that OpenAI has successfully reduced its AI model inference costs by 50% through deep system-level optimizations. This means OpenAI can now process twice as many inference requests within the same compute budget, or offer services at significantly lower prices.

According to the report cited from Phoenix News, the optimization goes beyond simple model compression or distillation — it involves comprehensive improvements at the system level, including kernel, scheduler, and memory management. This hardware-software co-optimization approach provides a new pathway for efficient large model deployment.

Inference cost remains one of the key bottlenecks for large-scale commercialization of AI models. If confirmed, OpenAI's cost reduction would significantly lower its API pricing, benefiting downstream developers and enterprise customers while putting pricing pressure on competitors.

OpenAI has not yet issued an official response to the report.