Optimize Slurm GPU Allocation for High Performance AI Workloads

The most common mistake in Slurm configuration is a lack of specificity. Using a generic --gres=gpu:1 flag tells the scheduler to grab any available card, which is a recipe for disaster in heterogeneous environments. If your cluster mixes H100s, A100s, and L40s, a researcher might accidentally land a small inference task on your most expensive training hardware. You must define resources with surgical precision in your slurm.conf and gres.conf files.

Start by naming your resources explicitly. Instead of a generic GPU label, use gres/gpu:h100 or gres/gpu:a100. This allows the scheduler to match the workload requirements to the specific hardware capabilities, such as memory bandwidth or Tensor Core generations. According to a 2025 report on HPC efficiency, clusters using explicit resource naming saw a 15 percent improvement in job-to-hardware alignment compared to those using generic labels.

• Define the Node

  In slurm.conf, specify the exact count and type: NodeName=gpu-node01 Gres=gpu:h100:8.

• Configure the GRES file

  In gres.conf, map the physical device files: Name=gpu Type=h100 File=/dev/nvidia0.

• Enforce Selection

  Train your team to use the --gres=gpu:type:number syntax to prevent resource mismatch.

Beyond naming, you should implement AutoDetect=nvml in your gres.conf. This allows Slurm to query the NVIDIA Management Library directly, reducing manual configuration errors and ensuring that the scheduler always has an accurate view of the available hardware. When you are building a sovereign cloud, this level of transparency is not just a performance boost; it is a requirement for auditability and resource accounting.

Not every AI task requires a full 80GB H100. Running a small preprocessing script or a lightweight inference model on a dedicated high-end GPU is a waste of resources. This is where Multi-Instance GPU (MIG) and Multi-Process Service (MPS) become essential. MIG allows you to partition a single physical GPU into up to seven independent hardware instances, each with its own high-bandwidth memory and compute cores.

Implementing MIG within Slurm requires a shift in how you think about resource requests. You are no longer requesting a GPU; you are requesting a specific slice. This is particularly powerful for multi-tenant environments where isolation is critical. Unlike software-based sharing, MIG provides hardware-level isolation, ensuring that a memory leak in one researcher's container does not crash the entire physical card. This is a core component of the Lyceum Cloud architecture, where we prioritize both efficiency and security.

If your workload involves many small, non-isolated tasks, MPS might be the better choice. MPS allows multiple processes to share the same GPU context, which reduces the overhead of context switching. However, it lacks the strict memory protection of MIG. For most enterprise AI teams, we recommend a hybrid approach: use MIG for development environments where multiple users share a node, and reserved full-GPU instances for large-scale distributed training.

1. Enable MIG

   Use nvidia-smi -mig 1 on your nodes.

2. Partition

   Create profiles like 1g.10gb or 3g.40gb based on your typical workload sizes.

3. Slurm Integration

   Update your gres.conf to recognize these partitions as unique allocatable resources.

A static queue is a slow queue. To truly optimize Slurm GPU allocation, you need a dynamic scheduling strategy that prioritizes high-impact jobs while keeping the hardware warm with smaller tasks. Backfilling is the secret weapon here. It allows the scheduler to start lower-priority jobs if they can finish before the resources are needed for a higher-priority job that is currently waiting for other resources to become free.

To make backfilling effective, your users must provide accurate --time limits. If every job is submitted with a 48-hour limit but only runs for two hours, the backfill scheduler will assume the node is occupied and leave it empty. We recommend implementing a PriorityWeight system that rewards shorter jobs or those using fractional GPUs. This encourages researchers to optimize their code and resource requests.

Preemption is another powerful tool, though it requires a culture of checkpointing. By creating a 'low-priority' queue for non-urgent research, you can ensure your cluster stays at 100 percent utilization. When a high-priority production training job arrives, Slurm can automatically suspend or kill the low-priority jobs, which then resume once the resources are free. This 'scavenger' model is how the world's most efficient clusters operate. According to documentation from NERSC, aggressive backfilling and preemption policies can increase overall cluster throughput by over 25 percent.

"Efficiency is not just about speed; it is about ensuring that no watt of power is wasted on an idle chip." - Maximilian Niroomand, CTO of Lyceum Technology.

The reality is that most AI researchers are not Slurm experts. They want to write code, not debug sbatch scripts. This friction often leads to over-provisioning: a researcher requests four GPUs 'just in case' when their model only utilizes two. At Lyceum, we solved this by building the Automated GPU Configuration Predictor and our Protocol3 orchestration layer.

Our platform analyzes the model architecture and dataset size before the job even hits the cluster. It suggests the optimal GPU type and count, preventing the common Out-of-Memory (OOM) errors that plague manual submissions. By moving the complexity into a software layer, we allow teams to focus on the AI while we handle the hardware optimization. This is especially critical for European startups that need to compete with the scale of US hyperscalers without the same massive budgets.

Our VS Code Extension further simplifies this by allowing one-click deployment. Instead of SSHing into a head node and manually managing environments, the developer stays in their IDE. The extension handles the containerization, data syncing, and Slurm submission. This radical transparency in the stack ensures that every engineer knows exactly what resources they are using and why. It turns infrastructure from a bottleneck into a competitive advantage.

You cannot optimize what you do not measure. Standard Slurm logs tell you when a job started and ended, but they do not tell you if the GPU was actually doing work. Integrating NVIDIA DCGM (Data Center GPU Manager) with a monitoring stack like Prometheus and Grafana is non-negotiable. You need to track metrics like DCGM_FI_DEV_GPU_UTIL and DCGM_FI_DEV_MEM_COPY_UTIL in real-time.

If you see high GPU utilization but low memory bandwidth usage, your bottleneck is likely data loading, not compute. In this scenario, adding more GPUs will actually decrease your efficiency. You should instead look at your NVMe storage throughput or your InfiniBand interconnects. This holistic view of the system is what differentiates a standard IT shop from a high-performance AI lab.

Finally, we must address the strategic importance of where these workloads run. For European enterprises, data sovereignty is a top-tier priority. Using US-based clouds often means navigating complex legal frameworks and risking vendor lock-in. By optimizing Slurm on sovereign European hardware, you ensure compliance with local regulations while maintaining the performance levels required for state-of-the-art AI. Lyceum Technology provides this bridge, offering the efficiency of a modern AI platform with the security of a sovereign cloud. We believe that the future of AI in Europe depends on our ability to build and manage our own high-performance infrastructure without compromise.