👉 Dosage computing is an innovative approach to managing and optimizing the computational resources required by machine learning models, particularly those with large parameter counts. It involves dynamically allocating and adjusting the computational resources—such as CPU, GPU, memory, and storage—based on the specific needs of a given model or task at any given time. This is achieved through intelligent algorithms that monitor the workload and performance of the model, ensuring that resources are efficiently utilized without over-provisioning or under-provisioning. By doing so, dosage computing aims to reduce costs, improve model training and inference efficiency, and enable the deployment of complex models on a wider range of hardware platforms. This approach is especially crucial in cloud computing environments where resource allocation can significantly impact performance and expenses.
