October 2026 Summaries

Modal has made its multi-node GPU offering, Modal Clusters, generally available, enabling developers to request clustered workloads through the `@modal.clustered` decorator while retaining integrations with Modal Volumes, Cloud Bucket Mounts, and Queues. The service targets large-scale AI training and inference workloads by providing on-demand GPU clusters, second-by-second billing, automated GPU and RDMA health management, and InfiniBand-based RDMA networking of up to 6.4 Tbps, with configuration for PyTorch and NCCL handled through an `rdma=True` flag. Supporting clusters required Modal to develop a gang scheduler that evaluates fleet-wide capacity and places all nodes of a workload together, rather than scheduling machines independently. Modal also added RDMA support to its secure gVisor runtime and upstreamed the changes, allowing direct GPU-memory networking without staging data through host memory. Customers including Decagon, 1X, and Runway are using the clusters for large-model fine-tuning, robotics world-model training, and distributed video-generation inference, while access is available to all Modal workspaces within plan-based GPU limits.