AI Training and Inference Planning for Indian Teams

Separate training, fine-tuning, and inference needs

Use the workload phase to guide the planning conversation. H100 80GB each can frame training discussions, H200 141GB each can frame high-memory AI, and L40S 48GB can frame inference, vision, or rendering. These are examples only, not confirmed availability.

• State whether the work is pre-training, supervised fine-tuning, evaluation, or serving.
• Capture model size, sequence length, precision, batch targets, and memory headroom.
• Identify the datasets, checkpoints, artifact storage, and data-movement constraints involved.

Ask the distributed-training questions early

Multi-GPU design is more than a count of accelerators. Describe the parallelism approach, synchronization pattern, fault tolerance, job duration, and checkpoint strategy so capacity and environment assumptions can be reviewed together rather than inferred.

• Will the job use data, tensor, pipeline, or another distributed-training approach?
• What restart, checkpoint, experiment-tracking, and reproducibility requirements apply?
• Which framework versions, container images, libraries, and orchestration expectations need validation?

Plan inference and latency without assumed benchmarks

For inference, present the request profile and the business boundary around responsiveness. Do not substitute a generic benchmark for your own test plan. No latency outcome, region, network path, or deployment result is promised here; these items require scoping and confirmation.

• Specify request volume, concurrency, input and output sizes, and the target user experience.
• Describe any API, application, edge, or internal-system integration that affects deployment planning.
• Define the measurements, test data, acceptance criteria, and owner for performance evaluation.