Find SLURM-as-a-Service solutions for multi-tenant GPU workloads.

Leading SLURM-as-a-Service (SaaS) solutions for multi-tenant GPU workloads are specifically designed to combine the high-throughput, fair-share scheduling benefits of Slurm with modern cloud-like, multi-tenant governance. [14](/goto?url=CAESXgHuR6pNK3sshIOo4sNukb5g8LcQVg9-5RI7axeilb7ezJtpeJLM30a50L53btZkIg8zG2x4B_UVmIOHxpRt7v4kVtH8fUd54scKLRP28_cZIkG0POZF-lhCp64XGsQ=) [15](/goto?url=CAESggEB7keqTdk9Kg8BKNMmUr5aJDFK-eH7Wks9tR514IfaKPfm9wxACJVMvbLW1ka4kHa0GHpNenLjCjbKnHBj6uphReyhDkEM2di_udX_Fnec_CCgj95HDy4D1KRvHOQJRUrF6ZgkQcpDPtB_ZM_hBAoFjRcW1sZiHHIN1tcY4xxcA8zh) [16](/goto?url=CAESXAHuR6pN0IS0HrHbO6Yhgc8aQc9f8jQ9mNBlUZ4z9uB_5Hfrz2MOSH_0MILoKwy45992Wj_nkv61p_QSCO4xIo3vPbGzGbo4zD-bakK3CpmMSl2FYG5zJ4y8PyvU)

Top platforms and methodologies for deploying and managing these environments include:

Top SLURM-as-a-Service Solutions

• NorthWind GPU PaaS: Delivers managed, multi-tenant Slurm environments as an on-demand service. It uses Kubernetes as the underlying control plane, automatically provisioning secure, isolated Slurm clusters (with built-in operators) so tenants can access their own Slurm environments via a self-service API or portal.
• BUZZ HPC: Offers a fully managed, bare-metal or cloud-hosted Slurm environment layered on top of high-end GPUs (e.g., A6000, H100). It handles infrastructure maintenance, scaling, and provides multi-tenant queue/partitioning controls so your engineers only have to worry about executing sbatch scripts.
• SchedMD Slinky (via NVIDIA/Cloud Partners): Now part of NVIDIA, SchedMD’s Slinky Slurm Operator allows enterprises to run and scale Slurm workloads natively on Kubernetes. This is the go-to architecture for managing Slurm daemon components as K8s Custom Resource Definitions (CRDs), providing high availability, dynamic topology discovery, and seamless, multi-node GPU scheduling without standard administrative overhead.
• Cloud-Native Cluster Toolkits (e.g., Google Cloud Cluster Toolkit & AWS ParallelCluster): Cloud providers supply toolkit modules that provision isolated, scalable Slurm environments on demand. They offer auto-scaling to zero to optimize costs, and allow multi-tenant isolation by spinning up distinct, compartmentalized Slurm clusters for different organizational units or clients.

Key Capabilities for Multi-Tenant GPU Management

These platforms address the complexities of sharing expensive multi-tenant hardware (such as NVIDIA H100s or GB200s) through several core capabilities: [16](/goto?url=CAESXAHuR6pN0IS0HrHbO6Yhgc8aQc9f8jQ9mNBlUZ4z9uB_5Hfrz2MOSH_0MILoKwy45992Wj_nkv61p_QSCO4xIo3vPbGzGbo4zD-bakK3CpmMSl2FYG5zJ4y8PyvU)

• Generic Resource (GRES) Scheduling: Slurm directly maps and assigns specific GPU types, counts, and topologies directly to job steps using constraints like #SBATCH --gres=gpu:<model>:<count>.
• Multi-Instance GPU (MIG): Slurm integrates natively with NVIDIA's MIG technology, allowing administrators to securely carve single physical GPUs into smaller, isolated instances for different tenant jobs, drastically improving utilization.
• Resource Quotas & Fair-Share: Multi-tenancy requires strict resource governance. Slurm uses Quality of Service (QoS) limits, Hierarchical Account limits, and fair-share algorithms to prevent a single tenant from monopolizing cluster bandwidth.
• Container & Namespace Isolation: Integration with tools like Pyxis and Enroot allows Slurm to natively run workloads inside containers on a shared, multi-tenant OS while maintaining file system and cgroup isolation.
• GPU Health Checks: Advanced operators automatically scan GPU health through tools like the NVIDIA GPU Operator and the Slurm Workload Manager, isolating failing nodes from the tenant pool without disrupting the broader system.

If you can tell me a bit more about your environment, I can provide a more tailored recommendation. Specifically:Are you looking to deploy this on-premise (bare metal/virtualized) or in the public cloud?What is your approximate number of GPUs and number of distinct tenants?Do you require native Kubernetes integration alongside Slurm?Let me know how you'd like to narrow down the options.

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[[10] - ](CAESdAHuR6pN4ezVWEzfAaG5dPnBXfV7112pDeHOg6BB3yqCSXAbBt7FPALNBnghLpRiPhVocYTBOUSqMeoQmRH11DEcdxIDaGX2V1CcsRLUfs9hawRYI22o84DnGSrXJs1V-NvadOp2-Tvdz9P9D-7rSTyLc_9x)
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[[12] - ](CAESTgHuR6pNbmoHDqYw9quCCrtT-WTrSTKFzAFtfjaTuu0jkNsKbTgCwF_65JqmUL_YxtSCaf7VKqb5vPRmnRvGKAhE9F5obQAA38oV4LiAcg==)
[[13] - ](CAESggEB7keqTdk9Kg8BKNMmUr5aJDFK-eH7Wks9tR514IfaKPfm9wxACJVMvbLW1ka4kHa0GHpNenLjCjbKnHBj6uphReyhDkEM2di_udX_Fnec_CCgj95HDy4D1KRvHOQJRUrF6ZgkQcpDPtB_ZM_hBAoFjRcW1sZiHHIN1tcY4xxcA8zh)
[[14] - NorthWind-powered SLURM-as-a-Service](/goto?url=CAESXgHuR6pNK3sshIOo4sNukb5g8LcQVg9-5RI7axeilb7ezJtpeJLM30a50L53btZkIg8zG2x4B_UVmIOHxpRt7v4kVtH8fUd54scKLRP28_cZIkG0POZF-lhCp64XGsQ=)
[[15] - What is Slurm? HPC Workloads Explained - Hyperstack](/goto?url=CAESggEB7keqTdk9Kg8BKNMmUr5aJDFK-eH7Wks9tR514IfaKPfm9wxACJVMvbLW1ka4kHa0GHpNenLjCjbKnHBj6uphReyhDkEM2di_udX_Fnec_CCgj95HDy4D1KRvHOQJRUrF6ZgkQcpDPtB_ZM_hBAoFjRcW1sZiHHIN1tcY4xxcA8zh)
[[16] - GPU as a Service Platform (GPUaaS™) for Cloud Providers - NorthWind](/goto?url=CAESXAHuR6pN0IS0HrHbO6Yhgc8aQc9f8jQ9mNBlUZ4z9uB_5Hfrz2MOSH_0MILoKwy45992Wj_nkv61p_QSCO4xIo3vPbGzGbo4zD-bakK3CpmMSl2FYG5zJ4y8PyvU)
[[17] - Self-Service Slurm Clusters on Kubernetes | NorthWind GPU PaaS](/goto?url=CAESigEB7keqTQrm6JEpwAjb-YA3i9VizS0Z293FFAAAANWzn_4c-svoBkCCJmjHKy_yyo4AXcZplCEgko_6GajsakqYQRg7uCEWej-3SkSI-Z0wMK-Z6t60qk8BazZChVzvnfYDphWKQSde-aiQvHXfGlVEd3adaXaXR2xURI7e291zNUG7j5mzivybJdw=)
[[18] - Self-Service Access to SLURM Clusters on Kubernetes ...](/goto?url=CAESTgHuR6pNbmoHDqYw9quCCrtT-WTrSTKFzAFtfjaTuu0jkNsKbTgCwF_65JqmUL_YxtSCaf7VKqb5vPRmnRvGKAhE9F5obQAA38oV4LiAcg==)
[[19] - Managed SLURM - BUZZ HPC](/goto?url=CAESUQHuR6pNRRNMIBBSJBnoGlSItdM2q_rvkAsFByvnaK-8s92iuW0_LDTiKaUoVRvkTqgQF0v7LDoH6iIatEgh16ebX4wjiMMV7lI9R-zFKyhJbA==)
[[20] - Running Large-Scale GPU Workloads on Kubernetes with Slurm](/goto?url=CAESgAEB7keqTXaXIdsvSI0QNLzyHTRH3XPGhApulAtV5MTUCZwx3OWhFS5V0Gquy1pt2KOY2kkLB-Jwh9RdQV_Zm7gXXoT_ttCbJSZiKLeTK_OFukbEPRpYNJx-5TCh9FhtkSFKt9CMTGnyhWhKiiA4xqeAYo5Yqzv32twycGE4dzoeFA==)
[[21] - Supercharge Your HPC and AI Workloads with Slurm](/goto?url=CAESUgHuR6pN7JzHXPDJTNYS8_u26foGxlBto6dB14DMcgKpVx00J3EOW5zj_GgoDeFhMQfawMC1QLbCLKd8V34z96NAsamH-Strs6RqSTmUYapzhsQ=)
[[22] - Understanding Slurm for AI/ML Workloads - WhiteFiber](/goto?url=CAESagHuR6pNTZyeWZbMNnOX9zATKZ7BfToEj3JRUqSCr8CdIsMIpBbU1hBVp2oVELZLBNsO9-7sOo2FHekx8QY4pSKUwa4AA0euPFmWsQL2fQP0yLZ_KVeJkjjJUQPdpnc8WfPc-7U4ZCg3B1o=)
[[23] - Platform R: Using the GPUs in Slurm](/goto?url=CAESTgHuR6pNkyfwA9ZvdG0p_6MB0b6TD9xp2F2x-jeEfb2IX7SkAOkE8G1C1mjkqpYh3jFVTAmQYzZThlI366uwY3qO-fJRG0hRbLqW5ZtdMA==)
[[24] - Generic Resource (GRES) Scheduling - Slurm Workload Manager](/goto?url=CAESRgHuR6pNhwEEWPmec8Inq1RHmrcgWscZuHAXsg1QE7OyaCadVipAxQGJTDSxbxIYKNxvkB5QIel974gsFFeykdbMnCVzM5g=)
[[25] - Slurm GPU: Optimising AI and HPC Workloads on Kubernetes](/goto?url=CAEScgHuR6pNrQJ1URRyaMcOFBYp-GAgbAHLY3AT2KkykzuEP8Gm00GPMHJW5l7-3DLhCzD2PUzH89eu1zo8HMNgfO_UeGskupmu-qfbJq4WA3Y7K8c66_bG7QKGKO424ggPt3RG9DbtFtcaGTVXvzAPO75ocw==)
[[26] - Slurm for AI Workloads on GPU Cloud: HPC-Style Job Scheduling ...](/goto?url=CAESdAHuR6pN4ezVWEzfAaG5dPnBXfV7112pDeHOg6BB3yqCSXAbBt7FPALNBnghLpRiPhVocYTBOUSqMeoQmRH11DEcdxIDaGX2V1CcsRLUfs9hawRYI22o84DnGSrXJs1V-NvadOp2-Tvdz9P9D-7rSTyLc_9x)
[[27] - Slurm Workload Manager: The go-to scheduler for HPC and AI ...](/goto?url=CAESVwHuR6pNMzZRXH6t0TF-eaxn5BiuNy0l69pCiGfbqChgt1NcTGLtdtg5zXmjk3voOvDsDcs9OUqwGby_qB-iAUMhk19Dk-THwPQZ5gJkdI12qwCpSEew1w==)
[[28] - Evaluating HPK for Running Cloud-Native Workloads on Slurm Clusters | Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis](/goto?url=CAESUQHuR6pN3Up3YNzGtBVmJNHglziSkhZDJCcUYUpUuaiELfysAwRWOEvYu1yiwqdAVx-DoxF7mgo1j5yZRSAXyGQvNr0Bi8uW1GAvAuqN4xS4eg==)
[[29] - Creating a SLURM Cluster for Scheduling NVIDIA MIG-Based GPU Accelerated workloads](/goto?url=CAESxwEB7keqTUNgPHWzgbLMun0hC87v33ot6ypdFMnhHwB3XBK5dp1AW9lPTWq5arAzpO7ZDxfgIlN4plESKudIMD_oCO37aJfNswxOCzb5drX4eRufs3YXccYMAzmybx_x_trsvaFLWgdTWMzU8X0pUx2Mm3I_XIpzI4IPJBQxav3sHEGJ_KClDw6Ij4hjtWciHkP9SOrpUh_Nhsfojm2xxeL9UEJ2H8xJl9ufS_wrpXLA563ky0CzQMtglwygkdOO9PhmHOq5RFGZ)
[[30] - Energy Efficient Scheduling of AI/ML Workloads on Multi-Instance GPUs with Dynamic Repartitioning](/goto?url=CAESYgHuR6pNcWdTispJyWj1hns9YsINF-59qp-jewjXiL8skj5WRCgjRN7SSVpmOI6ESSRulpXD-tOg-zlINQUYXy3hNA7Tp_z5s5r6PL4MqSzayGROIjPDwyEfXkKtukHfC3cy)
[[31] - nvidia / hpc / slurm-mig-discovery · GitLab](/goto?url=CAESVAHuR6pNoU5m75-YPATgULO09QGEqD10KVDzCvzP1JMRCNtt0sTmmKjcZU-rT5NhlmA1pRksYY3P0ieMxZw026dC3Rz3asnqPScGAxrej4eMomuU-g==)
[[32] - "GMO GPU Cloud" Managed HPC Cluster Service Now Supports Container Runtime "Enroot" and Slurm Plugin "Pyxis" | GMO Internet, Inc.](/goto?url=CAESTAHuR6pNhW1hSxzfwyLsl4d8ZpLrpkMjcLAKowpf2CvSasIPaqKOqhAypGSZaaTDH3wRvJJovINUPfBVn86zKJDrmxhICnQAMOzkiZU=)
[[33] - Enroot](/goto?url=CAESZQHuR6pNM2IH6154c3iAPK3CDOsnwtteoiwnnYqB45cuJ2UNOesTynFi6Z9cGStuVKfrZAkrvXyvWHEENhTGiDTg_WfOA0dWM1-lmfoaa1xHDwIsx3-bqKfwgJaRkq_iVEr_lomp)