Slurm
Networking & InterconnectAn open-source workload manager that schedules jobs and allocates resources across the nodes of a cluster, long the standard for high-performance computing.
Eleveight AI's cluster supports the established scheduling tools teams already use, so training jobs can be queued and allocated across GPU nodes with the same Slurm workflows familiar from HPC.
Overview
When many users and many jobs share a large cluster, something has to decide what runs where and when. Slurm is that something for much of the HPC world: a workload manager that accepts submitted jobs, queues them, and assigns them to available nodes according to priorities and resource requests. It has scheduled work on many of the world's supercomputers and carries naturally into GPU clusters used for AI training.
How it works
Users submit jobs to Slurm describing what they need, how many nodes, how many GPUs, for how long, and Slurm places them in a queue and dispatches them to suitable resources as those become free. It tracks what is running, enforces fair allocation among users, and reclaims resources when jobs finish, keeping a busy cluster orderly and well-utilized rather than chaotically contended.
Why it matters
Large training runs are batch jobs that may occupy many GPUs for hours or weeks, and coordinating them across a shared cluster is exactly the problem Slurm was built to solve. Its long lineage in HPC means teams arrive already fluent in it, and supporting it lets them run on new infrastructure without re-engineering how they schedule and manage their work.
Use cases
- Scheduling large-scale training jobs
- Fair resource allocation across many users
- Batch job management on GPU clusters
- Bringing established HPC workflows to AI infrastructure