Cluster management software serves as the “brain” of your cluster—coordinating computing tasks, scheduling jobs, and managing resources across various node types. Here’s a breakdown of key platforms and what types of compute and remote devices each can handle:

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1. SLURM (Open‑Source Workload Manager)

• What it controls: Linux servers (bare-metal or VMs) with CPU, GPU, and generic accelerators

• Features: Job scheduling, resource allocation (including GPUs), fair-share, preemption, accounting, network-topology awareness developer.nvidia.comen.wikipedia.org+1github.com+1

• Scale: Used on ~60% of TOP500 supercomputers alibabacloud.com+3en.wikipedia.org+3developer.nvidia.com+3

• Integration: Works standalone or with Kubernetes for containerized workloads

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2. Kubernetes + NVIDIA GPU Operator

• What it controls: Containerized workloads on Linux servers with GPUs

• Features: Automates GPU drivers, runtimes, scheduling via Kubernetes

• Compute types: CPU-only, GPU (NVIDIA via GPU Operator, Ray for workload management) developer.download.nvidia.com+7docs.ray.io+7dev.to+7developer.nvidia.com

• When combined with SLURM: Enables hybrid orchestration—slurm jobs within Kubernetes en.wikipedia.org+15collabnix.com+15alibabacloud.com+15

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3. NVIDIA Base Command / Bright Cluster Manager

• What it controls: Heterogeneous clusters—DGX systems, GPU servers, x86/ARM CPUs, DPUs, Spectrum switches developer.nvidia.com+2nvidia.com+2docs.nvidia.com+2

• Scale: From a few nodes to supercomputer‐scale installations

• Features: GUI lifecycle management, provisioning, multi-resource orchestration including Kubernetes & SLURM nvidia.com+1en.wikipedia.org+1

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4. DeepOps (by NVIDIA)

• What it controls: Linux GPU clusters via containerized SLURM or Kubernetes stacks

• Features: Deployment scripts, infrastructure-as-code, queuing, monitoring

• Compute types: GPU-enabled CPU servers expressed via containers; heterogeneous nodes supported if using Bright github.com+10github.com+10nvidia.com+10

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5. GPUStack (Open Source)

• What it controls: Mixed hardware (NVIDIA, AMD, Apple, Intel, Qualcomm GPUs), and devices from servers to desktops and laptops dev.to

• Use cases: LLM serving, API access, metering, dashboard for cluster usage

• Compute types: Heterogeneous GPU hardware across various operating systems dev.to+2dev.to+2collabnix.com+2

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6. Other Notable Platforms

• xCAT: Enterprise Linux/AIX management for diskless clusters, remote power, BIOS, hardware alerting en.wikipedia.org

• Apache Mesos: General cluster manager (Linux/Unix), supports containerized workloads, data-center resource sharing en.wikipedia.org

• HTCondor: High‑throughput computing across both server clusters and idle desktops/laptops.cross-platform (Linux/Windows/Mac) en.wikipedia.org

• Proxmox VE: Manages virtualization (KVM, LXC) clusters with VM/container orchestration, HA, Ceph storage en.wikipedia.org

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Summary Table

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Choosing the Right Software

• Pure HPC / batch jobs: Go with SLURM (± GPU Operator for containers)

• Container-first / cloud-native: Use Kubernetes + GPU Operator, possibly with SLURM hybrid for batch workloads

• Enterprise AI + mixed infra: NVIDIA Bright or Base Command Manager

• LLM service on mixed devices: Try GPUStack

• Bare‑metal cluster provisioning: Use xCAT or DeepOps for infra automation

• Virtualization-heavy setups: Choose Proxmox VE for VM/LXC orchestration

• Distributed/desktop spare cycles: Consider HTCondor or Mesos

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Next Steps

Want help picking the best fit?

• Choose your primary compute type (batch, container, VM, LLM service)

• Determine if you need heterogeneous device support (GPUs, desktops, servers)

• Decide on management scope (simple scheduling, infra provisioning, GUI, hybrid)

• I can guide you through setup steps for SLURM, Kubernetes GPU Operator, Bright, or GPUStack