The right AI infrastructure starts with a defined workload: what model will run, how it will be used, what data it will handle and what service level the business needs. A GPU specification by itself cannot answer those questions.
For Indian organisations moving from an AI demonstration to an operational service, a short requirements exercise can make a procurement discussion far more useful.
Separate development, training and inference
Development involves building and evaluating applications. Training and fine-tuning change model parameters. Inference runs a model to generate a result. These activities can have different requirements for memory, compute, storage and operating time.
A document-search assistant with occasional requests should not automatically inherit the infrastructure plan for a continuously used customer application. Describe each workload separately before combining capacity.
Build a workload brief
Give your technical team or infrastructure partner the following:
NVIDIA's inference sizing guidance similarly discusses workload behaviour, concurrency and latency as sizing inputs. Your final configuration still needs validation against your own application.
Look beyond the GPU
A practical architecture review should include CPU capacity, system memory, storage throughput, networking, power, cooling and physical installation constraints.
Also discuss the software environment: model serving, container management, updates, monitoring and access control. Identify who will maintain it after the pilot. An infrastructure purchase is incomplete if no team owns the production service.
If data must remain in a controlled environment, establish how source documents, model inputs, logs and backups will be handled. Avoid assuming that a local server alone resolves every privacy or governance question.
Compare on-premises, cloud and hybrid options
On-premises infrastructure may be considered where utilisation, data control and existing facilities support it. Cloud capacity may be useful for experiments or variable demand. Hybrid designs can combine approaches, but introduce integration and management requirements.
Compare alternatives over the same operating period. Include acquisition or rental, software, support, electricity, facilities, connectivity and staff effort. Do not base a decision solely on purchase price or an hourly GPU rate.
Require a proof of concept with acceptance criteria
Before committing to production capacity, agree on a small test using representative workloads. Measure the behaviour the business cares about: response time under load, output suitability, access boundaries and operational effort.
Document the model version, test data, concurrency and configuration. Results from another workload or a vendor benchmark should not be treated as a guarantee for your application.
Frequently asked questions
How much GPU memory does our AI application need?
It depends on the model, precision, context, concurrency and serving approach. Share these inputs and test a representative configuration rather than selecting memory from model size alone.
Should we buy hardware before completing the pilot?
Define the pilot architecture and acceptance criteria first. Where possible, evaluate suitable capacity before making a larger production commitment.
Can Layots help with an NVIDIA infrastructure discussion?
Explore Layots NVIDIA solutions and share your workload brief. Configuration, availability, licensing, support and commercial terms should be confirmed in the proposal.
Request a workload-led proposal
Tell us whether your project is development, fine-tuning or inference, the model you are considering and the expected number of users. This gives the discussion a concrete starting point.
Discuss your requirements with Layots
Layots Technologies supports businesses from its Chennai head office, with offices in Bengaluru and Hyderabad.
Request an IT assessment, call +91 99623 95939, message us on WhatsApp, or email info@layots.com. Share your business requirement and preferred discussion time so the team can understand the scope.
*By Layots Technologies Editorial Team.*