Why GPU cluster management matters
GPU capacity is expensive, scarce, and easy to waste. A cluster may appear busy while GPUs are starved by slow data loading, blocked by CPU or memory limits, or reserved for jobs that run only intermittently. For Indian startups, research teams, and enterprises balancing cloud bills with limited local capacity, how to manage GPU clusters for MLOps is an operational discipline—not merely a Kubernetes configuration task.
A well-run cluster should make four things predictable: researchers can obtain suitable capacity, production inference gets priority, experiments are reproducible, and finance can explain GPU spend. The same foundations also help teams operating local infrastructure, including deployments similar to hosting Sanjaya RLM on local GPU clusters in India.
Start with a capacity and workload inventory
Before installing an orchestrator, document what the cluster must run and how each workload behaves. Separate workloads by GPU type, memory requirement, duration, urgency, and network pattern:
- Training: long-running, checkpointed jobs that may need multiple GPUs and fast interconnects.
- Fine-tuning: often smaller jobs, but highly variable in duration and memory demand.
- Batch inference: interruptible workloads that can use spot or off-peak capacity.
- Online inference: latency-sensitive services requiring reserved capacity and health checks.
- Development: interactive notebooks and short experiments that should not block production.
Record GPU model, VRAM, node CPU and RAM, local NVMe capacity, network bandwidth, and whether GPUs support MIG or a comparable partitioning approach. Classify datasets and model artefacts as well: sensitive Indian customer data may require local processing, while public checkpoints can often use a managed cloud environment.
Define service-level objectives before buying capacity. Examples include maximum queue time for a training job, inference latency, checkpoint recovery time, and an acceptable percentage of idle GPU-hours. These measures make later scheduling and procurement decisions evidence-based.
Build a predictable Kubernetes foundation
Kubernetes is a common control plane for mixed MLOps workloads, but it should be introduced with clear boundaries. Use the NVIDIA GPU Operator or the vendor-equivalent stack to standardise drivers, container toolkit configuration, device plugins, and monitoring. Pin compatible driver and CUDA versions; an unplanned driver upgrade can invalidate production images or distributed training jobs.
Use labels and taints to express hardware and workload intent. For example, label nodes by GPU model and VRAM, taint production nodes so experimental pods do not land there, and use tolerations only where required. Request GPU resources explicitly in pod specifications rather than relying on shared, untracked access. Set CPU, memory, ephemeral-storage, and network requirements alongside the GPU request—GPU allocation alone does not guarantee performance.
Namespaces, resource quotas, and priority classes provide the first layer of multi-tenancy. Give online inference and platform services higher priority than exploratory notebooks, while ensuring that pre-emption does not destroy non-checkpointed training. A queueing layer such as Kueue or a batch scheduler can enforce fair sharing and admission control more reliably than allowing every job to start immediately.
Schedule for utilisation, not just allocation
A GPU marked “allocated” is not necessarily doing useful work. Track utilisation alongside memory use, power draw, data-loader throughput, CPU wait time, and inter-GPU communication. Low utilisation with high memory occupancy often indicates an oversized model reservation; low utilisation with low memory occupancy may indicate input or synchronisation bottlenecks.
Adopt scheduling policies that reflect business priorities:
- Use gang scheduling for distributed jobs so a training run starts only when the required GPU set is available.
- Use fractional GPUs or MIG for compatible small inference and development workloads, but validate isolation and performance first.
- Use pre-emptible or spot capacity for checkpointed experiments and batch inference.
- Reserve capacity for production rather than allowing research queues to consume the entire cluster.
- Schedule large jobs during lower-demand periods when latency requirements permit.
Set queue-time and runtime limits. A job that has waited for days may be incorrectly specified, requesting a rare GPU type or too many devices. Automated rejection messages and right-sizing suggestions are more useful than silent queueing.
Make observability actionable
A useful GPU dashboard answers three questions: what is running, who owns it, and why is it slow? Export DCGM metrics through Prometheus and visualise them in Grafana. At minimum, monitor GPU utilisation, memory utilisation, temperature, power, throttling, ECC errors, XID errors, pod identity, node health, and job queue time.
Add application-level metrics: tokens per second, samples per second, batch latency, request queue depth, checkpoint duration, and data-loader wait time. Correlate these with experiment IDs and model versions so an engineer can compare runs rather than inspect isolated node graphs. Alert on sustained hardware errors, thermal throttling, failed health checks, and abnormal idle allocation—not on every short utilisation dip.
Keep logs, metrics, traces, model artefacts, and deployment metadata linked through a run ID. This makes rollback and incident analysis faster and supports the same operational discipline used in broader AI-driven vulnerability management systems in India, where ownership and auditability matter as much as automation.
Control cost and capacity
GPU cost management starts with measurement. Calculate cost per training run, successful fine-tune, million inference tokens, or thousand production requests—not only cost per instance-hour. Include storage, egress, orchestration overhead, idle reservations, and engineering time where material.
Introduce a chargeback or showback model by team, project, and environment. Apply automatic shutdown to idle notebooks, expiry policies to temporary environments, and retention rules to checkpoints and container images. Use a tiered policy:
- Guaranteed: reserved for production and deadline-sensitive training.
- Standard: normal queue-based access for team workloads.
- Interruptible: spot or pre-emptible capacity for restartable jobs.
Scale horizontally when jobs are parallelisable and capacity is available; scale vertically when memory or interconnect requirements make additional nodes inefficient. For Indian teams, compare local cluster ownership with cloud or colocation using full operating cost, electricity, cooling, support, import lead times, data-residency needs, and availability—not GPU sticker price alone.
Secure multi-tenant operations
Treat model weights, training data, credentials, and notebook sessions as production assets. Use private registries, image scanning, short-lived cloud credentials, network policies, and least-privilege service accounts. Separate customer data from shared scratch volumes, encrypt persistent storage, and record access to datasets and model artefacts.
Do not allow privileged containers or host filesystem mounts by default. Keep GPU drivers and node operating systems patched in a maintenance window, and test upgrades against representative training and inference images. Back up cluster configuration, experiment metadata, and checkpoints; a GPU cluster without recoverable state is not resilient.
A practical operating runbook
A small platform team can establish a repeatable routine:
1. Daily: review failed jobs, queue time, idle allocation, hardware alerts, and production latency.
2. Weekly: right-size requests, remove stale artefacts, compare utilisation by team, and inspect cost anomalies.
3. Monthly: test node failure recovery, checkpoint restoration, access controls, quota fairness, and driver compatibility.
4. Before major training: validate dataset locality, image versions, checkpoint capacity, network topology, and rollback plans.
5. After incidents: capture the root cause, affected workloads, lost GPU-hours, and a concrete prevention task.
Keep cluster configuration in Git and deploy changes through reviewable infrastructure-as-code. Link runbooks and ownership to services so an on-call engineer can act without reconstructing the architecture. Teams building developer workflows may also benefit from a best AI task management tool for developers, provided it complements—not replaces—the cluster scheduler and incident process.
FAQs
Should every MLOps team use Kubernetes?
No. A single research workstation or one inference server may be better served by Docker and a simple queue. Kubernetes becomes valuable when multiple users, GPU types, environments, or availability requirements justify the operational overhead.
What is the most important GPU metric?
There is no single metric. Pair utilisation with memory, throughput, queue time, and application latency. High utilisation can still represent inefficient work, while low utilisation may be correct for a latency-sensitive service.
How do I prevent one team from monopolising GPUs?
Use namespaces, quotas, priority classes, queue-based admission, fair-share scheduling, and separate production capacity. Review allocations against actual completion rates rather than relying on informal agreements.
How much automation should be added first?
Start with reproducible images, explicit resource requests, quotas, telemetry, and checkpointing. Add autoscaling, pre-emption, and advanced partitioning only after baseline workload data is trustworthy.