New OpenAI technique cuts LLM inference FLOPs 50-70% by pruning low-impact circuits, enabling faster, cheaper deployment with little accuracy loss.
Circuit-Sparsity is OpenAI’s innovative method to streamline large models by identifying and deactivating neural circuits with minimal impact on performance. It achieves 50-70% reductions in computational demands, making inference faster and more cost-effective for widespread use[1].
This addresses key barriers in LLM scalability, especially for edge and enterprise applications where efficiency is paramount. By preserving accuracy, it bridges the gap between frontier model capabilities and practical deployment[1].
Paired with releases like GPT-5.2, such optimizations signal a shift toward sustainable AI infrastructure, reducing energy costs and enabling broader adoption in resource-constrained environments[1].
Source: ninjaai.com