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Cedana

GPU migration infrastructure for inference workloads

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Cedana is a GPU migration infrastructure platform that checkpoints, migrates, and resumes running inference and other workloads across instances, clusters, and clouds. It addresses the problem that AI workloads cannot move once running, which can leave GPUs idle, lose progress on failures, and keep infrastructure rigid. The platform supports training, inference, and HPC workloads, including CPU and GPU containers, distributed jobs, Kubernetes pods, and SLURM batch workloads, with no code changes or manual checkpoint hooks.

The core lifecycle is save, migrate, and resume, exposed through one API. It works natively with Kubernetes and SLURM, supports distributed workloads transparently with MPI and NCCL, and deploys via a Kubernetes helm chart or SLURM plug-in. Capabilities include policy-based automation, stateful reliability across node loss and preemption, running on spot instances, moving jobs based on live signals, and resizing compute across instances, clusters, regions, and clouds without losing progress. Integrations and platforms named include AWS, Google Cloud, NVIDIA, Kubernetes, SLURM, and Nvidia Dynamo.

Cedana is aimed at teams running AI and HPC workloads who need to move live jobs across their fleet. The pages describe scheduling a demo and reading documentation, along with a Kubernetes helm chart or SLURM plug-in for deployment; no free plan, trial, editions, or seat-based or usage-based packaging is stated.

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