Benefits of Separate Node Pools for Training and Inference

Training and inference both use GPUs, so it's tempting to run them on the same nodes. They behave very differently, though. Training jobs run for hours at full load and can restart from checkpoints. Inference services handle unpredictable traffic and need fast, steady responses. Separate node pools let you treat each appropriately.

Benefit 1: Predictable inference latency

A training job that saturates a node's GPUs, CPU and network can slow down an inference pod on the same machine. With separate pools, a large training run doesn't affect users.

Benefit 2: Cheaper capacity for training

Training jobs that checkpoint can run on Spot or preemptible instances, often at a large discount. Inference usually needs on-demand capacity. Separate pools make the pricing choice explicit for each.

Benefit 3: The right hardware for each job

Training often benefits from large multi-GPU instances with fast interconnects. Inference for many models runs well on smaller, cheaper cards. Matching instance types to workloads stops you paying training prices to serve requests.

Benefit 4: Different disruption rules

Inference pools get Pod Disruption Budgets and careful node upgrades. Training pools can accept interruptions and aggressive scale-down, as long as jobs resume from checkpoints.

Benefit 5: Clearer costs

When each pool has its own labels and cost tags, reports show training and serving spend separately, which is usually what finance teams ask for.

How to set it up

Label and taint each pool, then target it from workloads:

nodeSelector:
  workload: inference
tolerations:
  - key: workload
    value: inference
    effect: NoSchedule

With Karpenter, create one NodePool per workload type. With managed node groups, create one group per type.

When it isn't worth it

• One or two GPUs. Splitting them leaves both pools idle much of the time. Use scheduling priorities instead.
• Occasional fine-tuning. If training happens once a quarter, run it on temporary capacity rather than keeping a pool.
• Managed training services. If training runs on SageMaker or Vertex AI, it's already separate.

Look at last month's GPU usage split by workload before deciding. The numbers usually make the choice obvious.