Parallax is an open-source distributed model serving framework that enables organizations to deploy, scale, and serve large machine learning models across clusters of machines. It focuses on routing inference requests, distributing model computation across multiple GPUs/nodes, and enabling deployment on cloud, on-premises, or hybrid infrastructure. Parallax's value comes from allowing teams to host and manage multi-node model inference (including large models that exceed single-GPU capacity), improving throughput and enabling flexible self-hosted model serving without relying on a single cloud vendor.