Understanding Slurm for AI/ML Workloads

, WhiteFiber

Learn how Slurm powers large-scale AI/ML training with HPC-grade scheduling, and when to choose Slurm over serverless Kubernetes for your AI workloads

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Serverless Kubernetes has gained attention for its dynamic scaling and ease of use, but traditional high-performance computing (HPC) schedulers like Slurm remain vital for many AI/ML workloads. Slurm (Simple Linux Utility for Resource Management) is an open-source cluster management and job scheduling system implemented to run large-scale compute jobs on HPC clusters. In this post - the second in our series on infrastructure for scaling AI/ML - we explore Slurm’s architecture, how it supports AI/ML training at scale, and how it compares to a serverless Kubernetes approach.

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We’ll cover:

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  • Slurm’s Architecture and Components: How Slurm manages resources and schedules jobs in HPC environments.
  • Slurm in AI/ML Workloads: Why Slurm excels for large training jobs and multi-GPU tasks, and its core capabilities for researchers.
  • Comparisons to Serverless Kubernetes: Key differences in scheduling, scaling, and operations between Slurm and a serverless Kubernetes model.
  • Challenges and Limitations: Use cases that may not be well-suited for Slurm.
  • When to Use Slurm vs. Kubernetes: Guidance on which model fits different scenarios, and how hybrid approaches can combine the best of both.

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Slurm’s Architecture and Core Components

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Slurm follows a classic HPC scheduler design built for batch processing and long-running jobs. It consists of a central controller and multiple compute nodes, with jobs organized in queues and partitions:

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  • Controller (slurmctld):
    The central brain that monitors cluster resources and allocates them to jobs. It keeps track of available nodes, manages the job queue, and enforces scheduling policies. A backup controller can take over for high availability.
  • Compute Nodes (slurmd):
    The worker nodes where jobs execute. A daemon on each node waits for work, executes the assigned job tasks, and reports status back to the controller. Nodes are often grouped into partitions (resource pools) based on attributes like GPU capability or memory size.
  • Job Queue:
    Pending jobs wait in a queue until required resources are free. Slurm’s scheduler arbitrates resource contention by prioritizing jobs in the queue. It ensures fair allocation according to policies (e.g. user quotas or job priority).

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This architecture is optimized for maximum utilization of a fixed resource pool - typically a static cluster of servers. Slurm allocates nodes to jobs for the job’s duration, and those resources remain reserved until the job finishes. This guarantees consistent performance for each job, which is crucial for complex model training that might run for hours or days. Slurm is fault-tolerant and scalable, able to manage clusters from small labs to top supercomputers without kernel modifications.

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In a serverless Kubernetes setup, there is no fixed pool of always-on nodes; instead, the platform spins up and down worker nodes on demand and even scales to zero when idle. Kubernetes’ control plane (often managed by a cloud provider) handles scheduling of containerized workloads across these ephemeral nodes. This means Kubernetes naturally excels at elastic scaling and multi-tenancy (orchestrating many services or jobs dynamically), whereas Slurm’s traditional model assumes you have a dedicated cluster of nodes pre-provisioned. Slurm can integrate with cloud provisioning for elasticity (for example, adding nodes based on queue depth), but such capabilities are add-ons to its core design. In essence, Slurm was born in HPC’s static world, and Kubernetes in the cloud’s dynamic world - each architecture aligns with its origin.

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Slurm for AI/ML Workloads: Capabilities and Strengths

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Slurm has been widely adopted in academic and research labs and is now a popular choice for large-scale AI/ML training jobs. Its design offers several advantages for AI/ML workloads, especially for distributed training and GPU-intensive tasks:

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Optimized for Large Batch Jobs:

Slurm excels at orchestrating massively parallel jobs. It can schedule jobs that span dozens or hundreds of nodes, making it ideal for training deep learning models that require scaling out. By efficiently queuing and allocating resources, Slurm keeps expensive GPU nodes busy with minimal idle time for sustained throughput.

Strong Resource Guarantees:

Once Slurm allocates resources to a job, those CPUs, GPUs, and memory are reserved exclusively for that job’s duration. This guarantees reproducible performance without interference. Long-running training jobs benefit from this consistency - there’s no surprise eviction or resource contention once a job starts.

Excellent GPU Management:

Designed with HPC in mind, Slurm provides first-class support for accelerators like GPUs. Users can request specific GPU types and counts per job (e.g. #SBATCH --gres=gpu:V100:4 to request four V100 GPUs), and Slurm will schedule the job on nodes with those resources. This fine-grained GPU allocation is critical for deep learning workloads and is a native capability of Slurm. In fact, Slurm’s support for GPU computing has evolved to meet modern AI needs.

Mature Scheduling Algorithms:

Decades of use in HPC means Slurm’s scheduling algorithms are battle-tested for throughput and fairness. It can utilize advanced plugins for backfill scheduling, priority formulas, and resource limits - features that help maximize cluster utilization and enforce policies in multi-user environments. Many AI research labs appreciate these controls to balance cluster usage among teams.

Familiar to HPC Teams:

Researchers and data scientists in academic or national lab settings often have experience with Slurm (or similar batch schedulers). For such teams, using Slurm for AI projects allows the application of existing skills and workflows. Submitting an AI training job via a simple Slurm script (sbatch script) is straightforward, without needing to learn cloud-native DevOps concepts, thus lowering the learning curve to get started.

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Slurm’s success in AI/ML to date is evidenced by its adoption in numerous AI supercomputing initiatives. It is considered the de facto scheduler for many HPC clusters used by leading research organizations and AI companies. In practice, state-of-the-art language models and scientific AI simulations are frequently trained on Slurm-managed clusters that provide the raw horsepower and scheduling efficiency required for such large jobs.

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Challenges and Limitations of Slurm in AI/ML

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No solution is without trade-offs. Slurm’s HPC-centric approach brings some limitations for AI/ML workloads, especially when compared to more cloud-native orchestration:

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  • Not Ideal for Serving/Interactive Workloads: Slurm is geared toward batch processing. It isn’t well-suited for deploying live inference services or any workload that requires on-demand, low-latency request handling. Spinning up a REST API or a webservice on Slurm is possible but clunky - Kubernetes and serverless platforms handle those use cases far more gracefully.
  • Manual or Static Scaling: Traditional Slurm clusters run a fixed number of nodes, scaled up manually (or on a schedule). If your GPU cluster is at capacity, adding more nodes often means an admin provisioning additional servers or bursting to the cloud with custom scripts. While tools now exist to automate Slurm cluster expansion in cloud environments (Slurm for Artificial Intelligence & Machine Learning - SchedMD), it’s not as seamless as Kubernetes’ built-in autoscaling. There’s typically no concept of scaling down to zero when idle - resources might sit reserved (idle) until new jobs arrive.
  • Resource Inefficiency for Mixed Workloads: Because jobs reserve entire nodes or GPUs for their duration, there can be fragmentation and under-utilization. For example, a job that needs 3 GPUs will still occupy a 4-GPU node, leaving one GPU unused. Slurm’s focus is maximizing utilization across the whole cluster over time, but at any given moment you might see some resources stranded due to rigid allocations. In contrast, Kubernetes can pack multiple smaller pods onto a node to use all GPUs or CPU if possible, or scale nodes down. This difference in resource allocation strategy can make Slurm less cost-efficient for spiky or irregular workloads.
  • Limited Flexibility and Container Integration: Modern AI workflows often leverage containerized environments (Docker images) for consistency. Slurm can run jobs within containers (e.g. using NVIDIA’s Pyxis plugin for Docker integration, but this requires extra setup and isn’t as native as Kubernetes’ container-centric model. Also, extending Slurm to new use cases (like event-driven triggers or custom job types) is possible but not as plug-and-play as in the Kubernetes ecosystem. Slurm’s strength is in HPC batch jobs, and it “lacks universality” for other patterns compared to the more general-purpose Kubernetes.

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In summary, Slurm excels at what it was originally designed for - orchestrating large compute jobs on a static cluster. Kubernetes, on the other hand, was built with flexibility in mind and offers features like self-healing (restarting failed containers), fine-grained multi-tenant isolation, and integration with CI/CD pipelines that are not native to Slurm.

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Serverless Kubernetes - Complementary or Competitive?

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To put Slurm’s features in context, it helps to note what a serverless Kubernetes approach offers for AI/ML (as detailed in our previous post on the topic). In a serverless Kubernetes environment, compute resources scale dynamically with demand and management of the Kubernetes control plane is abstracted away. Key advantages include:

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  • On-Demand Scaling and Efficiency:
    Kubernetes can automatically scale out to handle a burst of training jobs or scale in when load drops, even down to zero nodes when idle. This pay-per-use model means you don’t pay for idle hardware, which is attractive for cost-sensitive AI projects or variable workloads.
  • Easier Model Deployment & Serving:
    Kubernetes-based frameworks (like KServe or Kubeflow) excel at deploying machine learning models as services. They handle rolling updates, canary deployments, and load-balanced inference out of the box. For use cases where an AI model needs to serve real-time predictions to users, Kubernetes is often the better fit.
  • Rich Ecosystem and Extensibility:
    The cloud-native ecosystem around Kubernetes offers a plethora of tools (monitoring with Prometheus, logging with ELK, GPU operators, etc.) and extensions for ML (e.g. Kubeflow for pipelines, Volcano for batch scheduling on K8s). This makes it easier to integrate AI workloads into a larger platform. However, using Kubernetes effectively requires DevOps expertise and comes with a steep learning curve for teams new to it.

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It’s clear that neither Slurm nor Kubernetes is a one-size-fits-all answer for AI/ML. In fact, neither was originally designed specifically for today’s machine learning demands - Slurm came from HPC batch jobs, and Kubernetes from cloud microservices. Slurm has adapted with things like GPU support to better serve AI, while Kubernetes’ extensibility allows it to be bent towards HPC-style jobs. The choice depends on the nature of your workload and operational priorities.

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Choosing the Right Tool: Slurm or Serverless Kubernetes?

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Which solution is right for you? That’s dependent on a number of variables and there isn’t a single right answer by use case. That said, here are some general guidelines to help the evaluation process.

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When Slurm Makes Sense

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Large-Scale Training Jobs:

If you run giant training jobs that consume many GPUs for days or weeks (e.g. HPC research, extensive hyper-parameter sweeps), Slurm’s robust scheduling and resource guarantees are ideal. It will ensure your multi-node job gets the dedicated time and resources it needs without interruptions.

Established Research Environments:

In academic or research lab settings where users are already familiar with HPC schedulers, Slurm provides a comfortable, proven environment. The workflows (job scripts, module loading, etc.) align with existing practices, so teams can be productive without re-tooling for Kubernetes. In addition, reproducibility is often critical in these environments which Slurm can help ensure.

Fixed Infrastructure / On-Premises Clusters:

If you have an on-premise GPU cluster or other fixed hardware, a Slurm setup lets you maximize utilization of that investment. Slurm excels at squeezing high throughput from a static resource pool, making it a good fit when you aren’t leveraging cloud elasticity .

GPU-Intensive Batch Workloads:

For projects that involve processing large datasets with GPUs in batch mode (e.g. training a vision model on a new dataset), Slurm’s first-class GPU scheduling and ability to handle MPI/distributed training jobs can be very advantageous. Complex multi-node GPU jobs run reliably under Slurm’s coordination.

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When Serverless Kubernetes Is the Better Fit

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Production ML Services and Inference:

When deploying ML models to serve predictions in production, especially with variable traffic, Kubernetes provides the needed dynamic scaling and high availability. For instance, an AI-powered web service can automatically scale out pods under load and back down to zero to cut costs during off hours.

Cost Optimization for Variable Workloads:

If your usage is sporadic or unpredictable, serverless Kubernetes helps avoid paying for idle GPUs. Workloads can be scheduled to run on-demand and the infrastructure disappears when not needed, enabling a true pay-as-you-go model. This is great for startups or teams on a tight budget.

Cloud-Centric and CI/CD Integrated Teams:

Teams with strong DevOps practices, who deploy on cloud and manage infrastructure as code, will benefit from Kubernetes. It integrates seamlessly with cloud services and CI/CD pipelines, providing a consistent environment from development to production. If your AI pipeline already lives in the cloud, adding a serverless Kubernetes for training or inference can be a natural extension.

Mixed Workloads and Pipeline Complexity:

Kubernetes can host not just training jobs, but also data preprocessing tasks, model serving, and even other app components in one environment. If your use case involves orchestrating many different steps (data ingestion, training, serving, monitoring) with varying resource needs, Kubernetes offers the flexibility to handle all of them on a unified platform.

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It’s worth noting that many organizations end up using both: Slurm for what it does best, and Kubernetes for what it does best.

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Hybrid Approaches: Leveraging Both Slurm and Kubernetes

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Rather than an either-or choice, a common pattern is to combine Slurm and Kubernetes to get the best of both worlds. Two common hybrid approaches are:

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Train on Slurm, Serve on Kubernetes:

In this model, you use Slurm to run the compute-intensive training phase on an HPC cluster, then deploy the resulting model to a serverless Kubernetes environment for inference serving. For example, a team might train a deep learning model on a multi-node GPU cluster managed by Slurm, output the model artifact to a shared storage or registry, and then load that model into a KServe inference service on Kubernetes. This way, training jobs enjoy Slurm’s efficient scheduling and GPUs, while end-users interact with the model through a scalable, low-latency Kubernetes service. Many AI workflows naturally split along these lines.

Bursting from HPC to Cloud:

Another hybrid approach is maintaining a base HPC cluster (managed by Slurm) and bursting to cloud Kubernetes for overflow capacity. Suppose your on-prem cluster handles normal workload, but occasionally you have a surge of additional training jobs - rather than queue them until you have available GPUs, you can burst those jobs to a cloud-based serverless Kubernetes cluster which will spin up extra nodes as needed. Slurm can even be configured to offload jobs to the cloud automatically when local resources are exhausted. This hybrid cloud strategy gives flexibility to meet peak demand without permanently expanding the on-prem infrastructure.

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Designing a hybrid solution does introduce complexity - for instance, coordinating authentication, data movement, and monitoring across two systems. Projects like SUNK (Slurm  on Kubernetes) are emerging to tightly integrate the two environments, effectively running Slurm’s scheduler inside Kubernetes. The goal is to allow organizations to seamlessly tap into Kubernetes-managed resources with Slurm’s scheduling intelligence. While still early, they highlight the industry’s recognition that HPC and cloud paradigms should complement each other rather than compete.

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Conclusion

Both Slurm and serverless Kubernetes offer powerful, yet distinct, solutions for AI/ML infrastructure. Slurm delivers proven capabilities for large-scale training workloads, with strong resource guarantees and efficient batch scheduling born from HPC experience. Kubernetes brings flexibility, automation, and a broad ecosystem that shines for deploying services and handling fluctuating workloads. The best choice depends on your specific requirements, team expertise, existing infrastructure, and workload characteristics. Key factors to evaluate include: the size/length of your jobs, whether you need on-demand scaling, the importance of cloud integration, and the tolerance for operational complexity.

In many cases, a hybrid approach provides the optimal balance - using Slurm where deterministic scheduling and HPC performance matter, and leveraging Kubernetes where agility and cloud features matter. By combining tools, organizations can achieve both high utilization of fixed resources and elastic expansion to cloud when needed, all while catering to the full lifecycle of AI/ML models. Careful planning is required to make such a dual strategy seamless, but the payoff is infrastructure tailored to each task.