What is Slurm and why it matters for HPCWhat is Slurm and why it matters for HPC

Slurm (Simple Linux Utility for Resource Management) is an open-source workload management system designed for high-performance computing clusters. Introduced in the early 2000’s, it has since become a standard in the HPC space. Today, it drives many of the world’s fastest systems — including Frontier, Polaris and Perlmutter.

From the beginning, Slurm was built for distributed systems with demanding performance and reliability requirements. It manages every aspect of workload orchestration — from scheduling and queue handling to priorities, quotas and license tracking. Its modular design allows for extensive customization, supporting plugins and configurations tailored to specific infrastructure needs.

Slurm also scales exceptionally well. It can handle tens of thousands of nodes and millions of active jobs while maintaining efficiency — a critical capability in HPC environments, where consistent performance under heavy load is key. Its flexibility and robustness have made Slurm one of the most trusted tools for managing large-scale computational tasks, from scientific research to real-world AI applications.

Why Slurm is gaining traction in ML workloadsWhy Slurm is gaining traction in ML workloads

While Slurm was originally built for HPC environments, its architecture makes it an excellent fit for modern machine learning workloads as well. From the outset, it was designed to manage long-running, resource-intensive jobs — exactly the kind of compute-heavy processes involved in training today’s large-scale models.

For ML teams, Slurm offers a high level of control and consistency. It lets engineers specify exactly where and how training jobs run — from selecting nodes with particular GPU types to accounting for interconnect topology and filtering out incompatible configurations. This level of granularity is essential for distributed training, where issues like node desynchronization or bandwidth bottlenecks can quickly derail an experiment.

Slurm also supports smooth collaboration in shared environments. Clusters can be configured so that multiple teams work side by side without interfering with each other. With priorities, quotas, preemption rules and access controls, organizations can ensure fair usage and reduce resource contention — all while keeping GPU utilization high and operations efficient.

When training foundation models or other compute-intensive systems, performance isn’t the only priority — reliability matters just as much. Slurm supports coordinated launches across nodes, tracks job states, and makes it easier to implement fault-tolerant strategies that keep training pipelines stable even in the face of hardware or scheduling hiccups.

Key features to look for in an AI job schedulerKey features to look for in an AI job scheduler

When training models, especially in distributed environments, the job scheduler becomes just as critical as your ML frameworks or datasets. It directly influences launch reliability, GPU efficiency and iteration speed. That’s why selecting the right orchestration tool deserves the same level of attention as choosing your model architecture.

A top priority is fine-grained resource control. A scheduler needs to consider not just the number of available GPUs, but also their specific characteristics — including type, interconnect topology, current utilization and hardware compatibility. In distributed training, these details can significantly impact performance: if jobs are scheduled on poorly connected nodes, synchronization may slow down or even fail.

Equally important is managing task dependencies. Model training typically includes multiple stages — from data preparation to training runs to logging and postprocessing. These steps need to launch in the right order and hand off parameters reliably between stages to keep pipelines reproducible and robust.

In shared environments, additional complexity comes into play. When multiple teams use the same cluster, the system should support priorities, quotas and preemption policies to ensure that critical workloads aren’t delayed. This helps minimize administrative overhead and reduces the risk of bottlenecks or resource contention.

Transparency is another key factor. Engineers need insight into how resources are being used, which jobs are running and where failures are occurring. A well-designed scheduler should integrate with monitoring systems and connect cleanly to your existing infrastructure — whether via APIs or by exporting metrics to external tools.

Slurm vs Kubernetes: Which scheduler is better for AI training?Slurm vs Kubernetes: Which scheduler is better for AI training?

Kubernetes has become the go-to platform for managing cloud infrastructure and containerized services. It excels at automating deployments, scaling applications and orchestrating production environments. But when it comes to training machine learning models — especially distributed workloads involving many GPUs — Kubernetes can run into architectural limitations.

A key challenge is resource awareness. Out of the box, Kubernetes doesn’t track GPU topology or detailed hardware characteristics unless explicitly configured. That creates a gap in training setups that rely on fast communication between GPUs — for example, synchronous training across multiple machines. Without extra tuning, Kubernetes may assign jobs to nodes with suboptimal interconnects. Slurm, by contrast, is built with hardware topology in mind. It understands the physical layout of the cluster and can allocate tightly coupled resources accordingly.

Another major difference is coordinated job execution. Kubernetes is optimized for stateless services that scale independently. But distributed training often requires jobs to be launched simultaneously across multiple nodes and treated as a single unit — which is essential for many ML frameworks. Achieving this in Kubernetes typically requires extra components or controllers. With Slurm, synchronized multi-node jobs are a built-in feature.

User experience also differs. Kubernetes workflows are built around manifests and APIs — a strong fit for DevOps teams, but not always ideal for ML engineers who want to launch and iterate on training runs quickly. Slurm streamlines these scenarios: engineers can specify parameters and dispatch jobs to the appropriate resources without intermediate steps or extensive infrastructure descriptions.

There’s also the question of operational overhead. Kubernetes relies on a persistent control plane and a set of supporting services that must remain healthy. This architecture adds complexity, especially in GPU-heavy environments where precise control and low latency are critical. Slurm, on the other hand, offers a leaner setup with direct scheduling and less background infrastructure — making it easier to scale when every node counts.

Slurm vs. Kubernetes for AI training: At a glanceSlurm vs. Kubernetes for AI training: At a glance

How to optimize Slurm for AI model trainingHow to optimize Slurm for AI model training

Slurm is well-equipped out of the box to handle resource-intensive workloads, but for ML-specific tasks proper configuration is key. Without it distributed runs may suffer from synchronization issues, idle GPUs or hard-to-trace failures.

The first step is fine-tuning how Slurm manages GPUs. It’s not enough for the scheduler to simply detect the number of available GPUs — it also needs to account for GPU types, interconnect topology and compatibility for distributed workloads. For example, to avoid communication bottlenecks, it’s best to schedule jobs on nodes with high-speed interconnects between GPUs.

Another critical factor is synchronized job execution. Many distributed training frameworks assume all processes start simultaneously across nodes. Even a small delay can break synchronization and cause the training run to fail. To prevent this, it’s best to use job modes where Slurm treats all entry points as a single unified process — ensuring coordinated launches and clean shutdowns with minimal risk of error.

Monitoring is equally important. Slurm supports collection of metrics around job runtime, GPU usage and task distribution. These can be exported to external observability systems, helping teams identify bottlenecks, troubleshoot jobs and prioritize workloads across shared resources.

It’s also essential to plan for failure recovery. Long-running training jobs can lose hours of progress due to a single crash. This can be mitigated with periodic checkpointing — saving model state and enabling resumption from the last successful step. If supported by the training framework, Slurm can pause and later restart jobs without reallocating resources or restarting from scratch.

Real-world applications: How organizations use Slurm for AIReal-world applications: How organizations use Slurm for AI

While Slurm originated in academic and research clusters, its reliability and scalability make it a go-to choice for industrial AI workloads — particularly those requiring large-scale training or fine-grained resource control.

In training large language models with billions of parameters, Slurm orchestrates execution across dozens or hundreds of GPUs. Coordinated execution helps ensure consistent environments and balanced resource use — both crucial for stable multi-day training jobs.

In computer vision, Slurm is used to manage high volumes of experimentation — such as evaluating models across datasets, tuning hyperparameters or testing variations. Job dependency management and efficient scheduling make it easy to automate these pipelines, without requiring manual coordination.

In multimodal projects, Slurm helps orchestrate complex pipelines involving heterogeneous resources. Some stages rely on CPUs, others on GPUs and still others on dedicated accelerators. Slurm handles the variability by assigning the right resource mix to each task while maintaining the overall system’s stability — even under heavy and highly diverse workloads.

Meet Soperator by Nebius: Making Slurm easy for AI workloadsMeet Soperator by Nebius: Making Slurm easy for AI workloads

Soperator is an open-source Kubernetes operator developed by Nebius to automate Slurm cluster deployment and management in cloud environments. It allows ML and HPC teams to leverage the power of Slurm while benefiting from Kubernetes-native features like autoscaling and high availability.

• Unified environment: A shared root file system ensures consistent environments across nodes, reducing manual syncs and easing setup.

• GPU health checks: Soperator automatically detects and isolates faulty GPUs, helping maintain cluster stability.

• Effortless scaling: Cluster size adjusts to workload demand — ideal for fluctuating AI training needs.

• High availability: Kubernetes-native HA keeps the cluster running even if components fail.

Soperator runs both in Nebius AI Cloud and other Kubernetes environments. A Terraform recipe is available for fast deployment on Nebius, streamlining infrastructure setup.

Common challenges when scaling Slurm for AI trainingCommon challenges when scaling Slurm for AI training

Even with a solid architecture, scaling a Slurm cluster for AI workloads requires attention to detail. As task density and GPU usage grow, so do the chances of hitting performance bottlenecks — especially during peak usage or when running many experiments in parallel.

One common issue is poor task placement. As job counts and resource diversity increase, some nodes may stay idle while others are overburdened. This often stems from default placement strategies that don’t account for fragmentation. Fine-tuning job policies helps even out resource use.

System overhead is another challenge. As clusters scale, the volume of job-related events grows, which can slow queue responsiveness. Adjusting logging levels, batching updates and tuning polling intervals can reduce this load.

Finally, rare failures may only surface under certain configurations or high-stress scenarios. That’s why robust observability, pre-production dry runs and gradual scaling are essential best practices.

Conclusion: The future of AI infrastructure with Slurm and NebiusConclusion: The future of AI infrastructure with Slurm and Nebius

As AI workloads become more complex, infrastructure needs grow accordingly — especially for distributed jobs running across many GPUs. Slurm remains a foundational tool, offering fine-grained control and seamless scaling across large clusters.

Still, running Slurm at scale involves careful setup, orchestration and recovery planning. That’s where infrastructure makes the difference. Nebius Soperator addresses this need by making Slurm clusters easier to deploy, operate and scale in cloud environments — so teams can focus on training models, not maintaining infrastructure.