When managing a GPU cluster and billing users, you’ll need a system that covers two core functions:

1. Cluster management: provisioning GPU resources, scheduling jobs, monitoring usage, enforcing quotas.

2. Billing/accounting: tracking user usage (time, GPU count, memory), applying rates, generating invoices.

---

️ 1. Cluster Management Tools

Open-source options

• Slurm (with NVIDIA DeepOps): Industry-standard workload manager for HPC/GPU clusters. Handles job scheduling, resource allocation, usage tracking, and accounting down to user/project level github.com+15gitee.com+15github.com+15.

• Kubernetes + NVIDIA KAI Scheduler: Runs GPU workloads containerized. Works at scale and integrates with billing tooling github.com.

• GPUStack: Lightweight open-source cluster manager built for deploying LLMs across GPUs en.wikipedia.org+8dev.to+8github.com+8.

• Genv: Enables per-user GPU environment isolation, quotas, monitoring pypi.org+2github.com+2genv.dev+2.

Commercial / enterprise options

• NVIDIA DCGM: Provides GPU health + utilization monitoring; integrates with schedulers like Slurm or Bright Cluster Manager hpc-gridware.com+15docs.nvidia.com+15developer.nvidia.com+15.

• Bright Cluster Manager: Fully managed GPU cluster suite with provisioning, monitoring, and GPU-specific diagnostics docs.nvidia.com+3developer.nvidia.com+3penguinsolutions.com+3.

• Penguin ICE ClusterWare & WhaleFlux: Enterprise platforms offering GPU provisioning, multi-tenant access, and billing support whaleflux.com.

---

2. Billing & Usage Accounting

Here’s how user billing typically works:

1. Usage Tracking: Schedulers like Slurm track GPU runtime, CPU, memory by user/project.

2. Rate Application: Multiply usage by rate (e.g., $/GPU-hour).

3. Aggregation & Billing: Export data and generate invoices automatically.

Examples:

• Slurm accounting + sacctmgr: lets you assign custom rates per partition or project, and export job/gpu time by user .

• Kubernetes + Prometheus: track GPU container usage, export metrics for billing.

• Commercial platforms (Penguin, WhaleFlux): include built-in usage dashboards and invoicing.

---

3. Example Architecture & Cost

Self-managed Stack (Slurm + DCGM)

• Compute nodes with RTX GPUs.

• Head node running Slurm + DCGM agents for GPU health.

• Users submit jobs via sbatch, specifying GPU count.

• Slurm enforces quotas, logs usage; DCGM monitors GPU status.

• Billing pipeline exports sacct, applies $/GPU-hr rate.

Pros: Flexible, no licensing cost
Estimated Setup: 1–2 days for experienced sysadmin
Monthly Ops: Minimal

Container-Based (Kubernetes + KAI/K8s GPU Operator)

• Containerized LLM inference or training workloads.

• GPU scheduling by KAI Scheduler.

• Metrics via Prometheus, billing via custom exporter.

Pros: Modern cloud-native, supports shards/microservices
Extra Work: Build billing exporter + invoicing system

Turnkey Enterprise Solutions

• Penguin ICE, WhaleFlux, Bright: have dashboards, multi-tenant UIs, built-in billing metrics.

• Cost: typically enterprise pricing—tens of thousands/year or per-GPU-license.

---

4. Billing Example Scenario

Suppose:

• You run 4× RTX 4090 GPUs.

• Rate: $2.00 per GPU‑hour to users.

• Max usage: 24 hrs/day × 4 GPUs = 96 GPU‑hrs/day.

• If fully booked: 96 × $2 = $192/day → $5,760/month.

Subtract operating cost:

• Power, cooling, overhead ≈ $1–2 per GPU‑hr → net profit ≈ $1 per GPU‑hr.

➡️ Potential profit: $2,880/month for 4 GPUs at max utilization.

Tip: sell to multiple users with quotas (e.g., each gets 100 GPU‑hrs/month), or unlimited with different tiers.

---

✅ Recommended Next Steps