Uber is expanding its partnership with Amazon Web Services (AWS) to deploy custom-designed chips for real-time trip processing and artificial intelligence (AI) model training.

As part of the initiative, Uber will use AWS’s Graviton processors to power its Trip Serving Zones—the core infrastructure responsible for matching riders with drivers and managing deliveries in real time. These systems process millions of data points within milliseconds, requiring high-performance, low-latency computing.

In addition, Uber has begun piloting Trainium chips, AWS’s specialised hardware for machine learning, to train AI models that power key features such as demand forecasting, route optimisation, and estimated arrival times.

The move is expected to enhance Uber’s ability to scale operations efficiently, improve ride-matching speed, and deliver more personalised user experiences across its platform.

By combining Graviton for real-time processing and Trainium for AI training, Uber aims to balance performance, cost efficiency, and scalability, while reducing reliance on traditional compute infrastructure.

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The partnership also reflects a broader industry shift, as enterprises increasingly adopt custom silicon from cloud providers to handle the growing demands of AI workloads and large-scale digital operations.

With millions of rides and deliveries processed daily, Uber’s investment in AI infrastructure underscores the critical role of advanced computing systems in powering next-generation mobility and logistics platforms.

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