The 22-Year-Old Software Running Large-Scale AI Training

How a job scheduler from 2002 became essential infrastructure for modern AI systems

Here is something that surprised me when I first learned it.

While most conversations about AI infrastructure focus on PyTorch, CUDA, and transformer architectures, the software actually running large-scale training jobs at major AI labs is older than Facebook, YouTube, and the iPhone.

It is called Slurm, which stands for Simple Linux Utility for Resource Management. Engineers at Lawrence Livermore National Laboratory created it in 2002, naming it after the fictional soda from Futurama.

According to SchedMD, the company that maintains Slurm, approximately 65 percent of the TOP500 supercomputers use it as their workload manager. Meta reportedly uses it across clusters with over 24,000 GPUs. OpenAI scaled it to 7,500 nodes for GPT-3 training.

For engineers transitioning from DevOps to AI/ML infrastructure, understanding Slurm is becoming increasingly relevant.

Kubernetes excels at orchestrating stateless microservices that scale horizontally. However, training large AI models presents different challenges that Slurm was designed to address.

Gang Scheduling: Distributed training across multiple GPUs requires all processes to start simultaneously. A delay of even a few seconds between nodes can break synchronization. Slurm guarantees that all resources for a job are allocated at the same moment.

Resource Reservation: Once Slurm allocates GPUs to a job, those resources remain reserved until completion. There are no surprise evictions mid-training. For runs that take weeks and cost significant compute resources, this predictability matters.

Hardware Topology Awareness: Slurm understands the physical layout of clusters, including which GPUs connect via NVLink and which nodes share InfiniBand switches. This awareness helps optimize communication patterns in distributed training.

The distinction is not about which tool is better overall. Many organizations use both: Slurm for training workloads, Kubernetes for model serving and inference APIs.

A common pattern in production AI infrastructure:

Slurm handles batch training jobs, distributed experiments, and long-running pretraining workloads.

Kubernetes handles model serving, inference endpoints, and production API deployments.

Hybrid tools like Slinky and Soperator bridge the two systems, bringing Slurm scheduling capabilities to Kubernetes environments.

Google Cloud, AWS, and Azure now offer managed Slurm cluster options, reflecting growing demand for HPC-style scheduling in AI workloads.

One notable aspect of Slurm is its simplicity. Here is a complete job script for distributed training across 64 GPUs:

#SBATCH --job-name=train-model
#SBATCH --nodes=8
#SBATCH --gres=gpu:8
#SBATCH --time=72:00:00

srun python train.py --model-size=70B

The SBATCH directives specify resource requirements. The srun command executes the training script across all allocated nodes. Save this as a file and run it with sbatch train.sh.

Five commands cover most daily Slurm usage:

Several cloud providers offer managed Slurm environments:

AWS ParallelCluster - Managed Slurm on AWS

Google Cloud Cluster Toolkit - Deploy Slurm clusters on GCP

Azure CycleCloud - Slurm on Azure with auto-scaling

Nebius Managed Soperator - Slurm-on-Kubernetes

For engineers with DevOps backgrounds, several skills transfer directly to Slurm:

Linux system administration experience applies directly. Slurm runs on Linux, uses standard logging, and follows familiar configuration patterns.

Shell scripting knowledge is immediately useful. Slurm job scripts are bash scripts with special comment directives.

Distributed systems concepts like job scheduling, resource allocation, and cluster management apply with minor adaptation.

Most engineers report becoming productive with Slurm basics within a week. The learning curve is concentrated upfront, then flattens quickly.

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