How Modal built their own container runtime, file system, GPU resource solver, and more

Modal operates a serverless cloud platform designed to simplify AI workload deployment by abstracting away infrastructure complexities like Kubernetes and YAML. To achieve sub-second container startup times, the platform utilizes a custom FUSE-based file system that lazily loads container images and leverages task profiling to pre-fetch necessary files. Managing multi-cloud GPU capacity involves solving complex linear programming problems to optimize for cost and availability while ensuring consistent hardware health across diverse providers. Security is maintained through gVisor sandboxing, which isolates untrusted code and prevents noisy neighbor issues. As the platform expands into multi-node training, it addresses the challenges of RDMA networking and inter-node communication. This infrastructure reflects a growing convergence between traditional supercomputing techniques and modern distributed systems, driven by the massive computational demands of large-scale AI model training.