How do I set up a GPU cluster for both training and inference?

Setting up a GPU cluster for both training and inference is mostly about balancing throughput, latency, scheduling, storage, and networking. A good design separates the two workloads while keeping them easy to manage.

1) Start with the workload split

Decide what you need for each:

• Training

  • Long-running jobs
  • High GPU utilization
  • Often needs multi-GPU or multi-node scaling
  • Heavy storage and checkpoint I/O

• Inference

  • Low latency or high throughput
  • Smaller, more frequent requests
  • Benefits from autoscaling and serving optimizations
  • Often uses different GPU types than training

If possible, dedicate some nodes to training and some to inference. Mixing them on the same nodes can cause noisy-neighbor issues.

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2) Choose the cluster architecture

Common patterns

A. Separate pools in one cluster

• One Kubernetes cluster or one Slurm cluster
• Distinct node groups:
  • training nodes
  • inference nodes
• Easier shared management
• Good if your team is small

B. Separate clusters

• One cluster for training, one for inference
• Best isolation
• More operational overhead
• Good for strict SLOs or different security needs

For most teams, one cluster with separate node pools is the best start.

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3) Pick hardware

GPU selection

• Training: prioritize VRAM, interconnect, multi-GPU performance
  • Examples: NVIDIA H100, A100, L40S depending on budget/workload
• Inference: prioritize cost/performance and latency
  • Examples: L4, L40S, A10, sometimes H100 if needed

Other hardware

• CPU: enough cores to feed GPUs
• RAM: especially important for data loading and inference preprocessing
• Storage: local NVMe on workers is very helpful
• Networking: at least 25/50/100 GbE; InfiniBand or RoCE for distributed training

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4) Use a scheduler/orchestrator

You need a system to allocate GPUs cleanly.

Options

Kubernetes

• Good for mixed training + inference
• Strong ecosystem for serving, autoscaling, observability
• Use:
  • NVIDIA GPU Operator
  • node pools/taints/tolerations
  • namespaces and quotas
  • Horizontal Pod Autoscaler / Cluster Autoscaler

Slurm

• Excellent for HPC-style training
• Great for batch and multi-node jobs
• Less convenient for production inference

Hybrid

• Kubernetes for inference, Slurm for training
• Common in larger orgs

If you want both on one platform, Kubernetes is usually the most flexible.

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5) Set up GPU scheduling

On Kubernetes, configure:

• NVIDIA drivers on nodes
• NVIDIA device plugin or GPU Operator
• Node labels like:
  • workload=training
  • workload=inference
• Taints/tolerations so inference pods don’t land on training nodes and vice versa
• Resource requests/limits for GPUs:
  • nvidia.com/gpu: 1

For finer sharing:

• MIG on supported NVIDIA GPUs for partitioning one GPU into slices
• Time-slicing if acceptable, though it’s less isolated

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6) Build the storage layer

Training and inference need different storage patterns.

Training storage

• Large datasets in object storage or distributed storage
• Fast local cache on nodes if possible
• Checkpoints saved to durable storage
• Common choices:
  • S3 / GCS / Azure Blob
  • NFS for smaller setups
  • Ceph / Lustre / BeeGFS for bigger clusters

Inference storage

• Model artifacts in object storage or model registry
• Fast startup by preloading models onto local disk or using a model cache
• Keep images and models versioned

A model registry is very useful to manage versioning and promotion.

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7) Set up networking

For training:

• Multi-node training benefits from low-latency, high-bandwidth networking
• Ensure NCCL works correctly
• Consider RDMA/InfiniBand for scale

For inference:

• Put an ingress or API gateway in front
• Use load balancing and possibly autoscaling
• Ensure observability on latency and error rates

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8) Use the right software stack

Training stack

• PyTorch or JAX or TensorFlow
• Distributed training:
  • PyTorch DDP / FSDP
  • DeepSpeed
  • Ray Train
• Job submission:
  • Kubeflow Training Operator
  • Volcano
  • Slurm job scripts

Inference stack

• vLLM
• NVIDIA Triton Inference Server
• TensorRT-LLM
• TorchServe
• BentoML
• FastAPI for custom serving
• For LLMs, vLLM or Triton is often a strong choice

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9) Add autoscaling

Training

• Usually scale by queue depth or submitted jobs
• Cluster autoscaler can add nodes when jobs are pending

Inference

• Scale based on:
  • request rate
  • latency
  • GPU utilization
  • queue length

For inference, you may want:

• multiple replicas per model
• warm pools of GPUs
• canary deployments for new model versions

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10) Put observability in place

Track:

• GPU utilization
• GPU memory usage
• temperature and power
• node health
• network throughput
• training metrics
• inference latency, throughput, error rate
• cost per job/request

Useful tools:

• Prometheus + Grafana
• NVIDIA DCGM exporter
• Loki / ELK for logs
• OpenTelemetry for tracing

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11) Secure the cluster

• Use separate namespaces or projects
• RBAC and least privilege
• Private container registry
• Secrets management:
  • Kubernetes Secrets, Vault, cloud KMS
• Image scanning and signed images if possible
• Network policies between training and inference services

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12) Operational recommendations

• Start with one small production inference pool and one training pool
• Keep training jobs preemptible if cost matters
• Keep inference nodes reserved for production traffic
• Use spot/preemptible instances for non-critical training
• Standardize container images for reproducibility
• Automate:
  • node provisioning
  • driver install
  • model deployment
  • job submission

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Example practical setup

A simple, solid setup:

• Kubernetes cluster
• Node pools:
  • 2–4 GPU inference nodes with L4 or L40S
  • 4–16 GPU training nodes with A100/H100 or similar
• NVIDIA GPU Operator
• vLLM or Triton for inference
• PyTorch + FSDP/DeepSpeed for training
• S3-compatible storage for datasets and models
• Prometheus/Grafana + DCGM exporter
• Cluster autoscaler
• MIG if supported and useful
• Taints/tolerations to isolate workloads

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Rule of thumb

If you want the shortest path:

1. Use Kubernetes
2. Create separate node pools for training and inference
3. Install NVIDIA GPU Operator
4. Use object storage for datasets and models
5. Deploy vLLM/Triton for inference
6. Use PyTorch Distributed/DeepSpeed for training
7. Add autoscaling and monitoring

If you want, I can also give you:

• a reference architecture diagram
• a Kubernetes-based setup checklist
• a Slurm-based alternative
• or a cost-conscious design for a small team