Most published AI infrastructure guidance describes training clusters, because that is where the impressive numbers are. Most enterprises are doing inference. The requirements diverge on nearly every axis.

Where they differ

Duration. Training is a long job that runs for days and must be restartable. Inference is millions of short requests that must each complete quickly. One is a batch problem, the other is a service problem.

Interconnect. Training nodes exchange gradients constantly and need a fast lossless fabric between them. Inference nodes, for models that fit on one node, do not talk to each other at all. The expensive east-west fabric that training requires is largely wasted on single-node inference.

Storage. Training reads datasets repeatedly at high concurrency and writes enormous checkpoints. Inference reads model weights once at startup and then barely touches storage. A storage design built for training is heavily over-specified for inference.

Memory. Training needs room for weights, gradients, optimiser state and activations, which is several times the model size. Inference needs weights plus the key-value cache for in-flight requests. The cache scales with concurrency and context length, and it is the thing people forget.

Utilisation. Training saturates accelerators for days. Inference is bursty and follows user behaviour, which means you are sizing for a peak and paying for the trough.

Failure. A failed training job restarts from a checkpoint and loses hours. A failed inference node loses in-flight requests and must be replaced immediately by something else. One needs checkpointing, the other needs load balancing and health checks.

The two common mistakes

Sizing inference like training. Buying the full training topology — high-end interconnect, a parallel file system, maximum-memory accelerators — for a workload that serves requests from a model that fits comfortably on one device. Expensive, and the extra capability sits idle.

Sizing training like inference. Buying nodes with adequate accelerators and an ordinary network, then discovering that multi-node training spends its time waiting on the fabric. The cluster works and delivers a fraction of its theoretical throughput.

What to ask before designing anything

  • What is the honest split between training, fine-tuning and inference over the next eighteen months?
  • For inference: concurrent requests at peak, acceptable latency, context length
  • For training: does a single job span multiple nodes, and how many
  • Does the model fit in one device’s memory at the precision you will use
  • Is there a batch component, and can it run at off-peak times

The fine-tuning answer is the interesting one, because fine-tuning sits between and is what most enterprises actually do. It is multi-node sometimes, checkpoint-heavy always, and much less demanding on interconnect than full pre-training.

The pattern that usually fits

A modest, well-specified training and fine-tuning cluster with a proper fabric, sized for the largest job you will genuinely run. Separately, an inference tier built for availability and scaled horizontally, with ordinary networking and no shared parallel file system.

Two designs, because it is two problems. The single-cluster answer that serves both is usually a compromise that is expensive for inference and constrained for training.

Why this keeps happening

Because the requirement arrives as “we need GPUs” and the reference material describes training. The corrective is the same as everywhere else in this job: ask what the thing actually does before deciding what it needs, and write the answer on the page where the sizing lives.