Dedicated GPU Capacity Planning for Indian Teams

Map workload requirements to a planning conversation

GPU selection starts with what the team needs to run. These examples help structure a conversation; they do not represent a live inventory, a reservation, or a confirmed configuration.

• L40S 48GB can frame inference, computer-vision, and rendering requirements.
• H100 80GB each can frame single- or multi-GPU training requirements.
• H200 141GB each can frame high-memory AI requirements.

Bring the buyer questions that affect capacity

A useful request makes memory, topology, timeframe, and commercial assumptions explicit. That enables Aitonomi AG to distinguish a short evaluation from a longer dedicated-capacity need and identify what needs further scoping.

• What model size, context length, batch size, precision, and memory headroom are required?
• Is the job single GPU, multi-GPU, or distributed, and what throughput or run window matters?
• What start date, rental period, renewal option, and budget approval process should be considered?

Set scope before commitment

Dedicated GPU capacity, managed-environment discussions, and deployment support are considered against the agreed requirement. Actual configuration, timing, software, data location, eligibility, commercial structure, and terms must be confirmed before any commitment.

• Request a written view of the proposed configuration and the assumptions behind it.
• Identify required access controls, storage patterns, and data-handling questions early.
• Review responsibilities, support expectations, and acceptance criteria with the commercial terms.