Model Placement

Model placement, the strategic allocation of machine learning models across computing resources, aims to optimize training speed and efficiency while managing memory constraints. Current research focuses on developing efficient algorithms for placing large models, particularly deep learning architectures like convolutional neural networks and large language models, across distributed systems, including edge networks and clusters of GPUs. This involves exploring techniques like parameter sharing to improve storage efficiency and adaptive placement strategies to handle the varying computational demands of different model components, such as those found in Reinforcement Learning with Human Feedback (RLHF) pipelines. Improved model placement significantly impacts the scalability and practicality of training and deploying increasingly complex AI models.