HAMi vs Slurm Workload Manager
HAMi
project-hami.ioSlurm Workload Manager
slurm.schedmd.comSide by side
| Figure | HAMi | Slurm Workload Manager |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · GPU Cluster Management Software | 8.3 (#5) | 7.5 (#11) |
| Purpose | HAMi is open-source, cloud-native GPU virtualization middleware for sharing, isolating, and scheduling heterogeneous accelerators on Kubernetes. | Slurm is an open-source, fault-tolerant, highly scalable cluster management and job scheduling system for large and small Linux clusters. |
Plans
In their own words
HAMi
HAMi is open-source middleware for sharing and scheduling GPUs and other accelerators among AI workloads on Kubernetes. Workloads can request GPU memory and core limits through Kubernetes resource controls. Scheduling options include binpack, spread and numa-first, with binpack and spread available at node or GPU scope. The supported-device list spans NVIDIA, AMD, AWS Neuron, Huawei Ascend, Cambricon, Enflame, Hygon, Iluvatar, Kunlunxin, MetaX, Moore Threads, Vastai and Biren; isolation and partitioning capabilities vary by device. HAMi works with Kubernetes APIs, DRA and CDI, and documents scheduling integrations including Volcano, Kueue, Koordinator and NVIDIA KAI Scheduler. Its node metrics endpoint exposes device memory and core utilization for Prometheus. Classic HAMi requires Kubernetes v1.23 or later. HAMi-DRA requires Kubernetes v1.34 or later, the DRA Consumable Capacity feature gate, CDI and NVIDIA driver 440 or later. NVIDIA limits use user-space library interception, which does not cover applications that bypass CUDA. Enforcement is described as soft and best-effort; the FAQ recommends MIG when hardware-enforced isolation is required.
Slurm Workload Manager
Slurm Workload Manager is free, open-source software for allocating compute resources and scheduling work across Linux clusters. It starts and monitors jobs on allocated nodes and manages queues of pending work. Optional plugins add accounting, advanced reservations, gang and backfill scheduling, topology-aware resource selection, resource limits, and multifactor job prioritization. The system uses a central slurmctld manager and slurmd daemons on compute nodes; accounting and REST components are optional. GPU plugins support NVIDIA, AMD, Intel, AWS Trainium, and AWS Inferentia accelerators, along with NVIDIA MPS and GPU sharing. Accounting can use MySQL or MariaDB and can record data in a text file, directly in a database, or through slurmdbd for multiple clusters. Slurmrestd provides REST access, but the API is not designed for direct internet exposure and supports only unencrypted HTTP. Linux is thoroughly tested on arm64, ppc64, and x86_64; FreeBSD and NetBSD support is limited, and macOS is unsupported. Deployment is self-hosted.