OpenAI Cuts Inference Costs by 50% with New Optimization Technique

OpenAI has found a way to reduce its inference costs by roughly 50%, a development that could reshape the economics of running large language models at scale.

Inference is the process of actually running a trained AI model to generate responses. Every ChatGPT query, every API call, every AI-generated response burns compute.

This cost reduction arrives amid a company-wide offensive against compute expenses. In June 2026, OpenAI unveiled a custom inference chip called Jalapeño, co-developed with Broadcom. The chip is designed to deliver better performance-per-watt for high-demand language model applications like ChatGPT, while simultaneously reducing OpenAI’s dependence on Nvidia’s GPUs.

OpenAI already offers its Batch API at a 50% discount compared to standard API pricing. The catch: those workloads run asynchronously within a 24-hour window, meaning they’re not suitable for real-time applications.

The industry more broadly has been deploying a toolkit of optimization strategies. Techniques like quantization, which reduces the precision of model weights to shrink computational demands, and caching, which stores frequently requested outputs to avoid redundant computation, have achieved cost savings of 50% or more across multiple providers. Mixture-of-Experts architectures, where only a subset of a model’s parameters activate for any given query, represent another approach that has delivered meaningful efficiency gains.

For OpenAI specifically, the math is existential. Historical estimates suggest inference expenditure alone may reach billions annually, and that’s before factoring in the continued scaling of models and the explosive growth of ChatGPT’s user base.

The custom chip strategy with Broadcom signals that OpenAI views this as a long-term structural challenge. Building your own silicon is a multi-year, multi-billion-dollar commitment.

For the crypto and decentralized computing space, as centralized AI providers drive down their operational costs, decentralized compute networks face a moving target. If running models becomes less expensive across the board, the cost premium of decentralized alternatives shrinks in relative terms.

Google, Meta, Amazon, and Microsoft are all pursuing their own chip programs and optimization strategies.