EDITORIAL DRAFT — outline only. Not ready for publication. AI infrastructure decisions, part 2 of 6. Audience: AI developers and technical managers defining a workload.
Training and inference answer different questions
[Explain training, fine-tuning and inference with one concrete application. Avoid treating all inference as small or all training as a multi-GPU problem.]
Turn the workload into infrastructure requirements
- [Training: model and dataset size, memory, checkpoint storage, interconnect, job duration and recovery.]
- [Inference: model size, precision, context length, concurrency, batching, latency targets and throughput.]
- [Both: utilisation, availability, data location, observability and the team’s operational capacity.]
Two worked workload profiles
[Compare an explicitly hypothetical batch job with an interactive application. Show which requirements change and why. Do not invent measured throughput or use GPU count alone as a recommendation.]
Evidence and assumptions to publish
[Source framework and hardware documentation. Record hardware, model/version, precision, utilisation, workload, software versions and measurement date for each numerical comparison. Distinguish estimates, vendor claims and reproducible measurements.]
Reader next step
[Create a workload checklist covering deployment stage, model, memory, traffic, latency, location, budget and timing. Intended CTA: complete the checklist. Publish it inline or as a real downloadable resource before linking to it; do not require newsletter signup. Add adjacent series links only when live.]