AI GCC in India · AI Infrastructure & GPU
NeoIntelli helps teams design, source, provision and operate the infrastructure needed for AI development, training, fine-tuning and inference across cloud, reserved and dedicated GPU environments.
An AI GCC does not automatically require dedicated GPUs. The right compute model depends on development, training, fine-tuning, inference, utilization, latency, data sensitivity and cost. NeoIntelli plans capacity from the workload, chooses between cloud on-demand, cloud reserved, dedicated and hybrid per stage, provisions developer, training and inference environments, and runs AI FinOps so the GCC's compute spend is visible, allocated and reviewed.
Decision model
The accelerator is the last decision. These factors come first, and they change the answer more than the hardware generation does.
Architecture
Each stage has a different demand shape. Sizing all three to the peak of one is how compute budgets fail.
STEP 01
Development
Developer environments, model and API access, vector infrastructure, experiment tracking.
STEP 02
Training and fine-tuning
GPU capacity planning, pipelines, storage, checkpoint management.
STEP 03
Inference
Serving, autoscaling, model gateways, observability, security.
Runs on
Compute models
No prices here on purpose. Pricing changes by provider, region, accelerator and commitment; the trade-offs do not.
| Model | Best for | Advantage | Trade-off |
|---|---|---|---|
| Cloud On-Demand | Experiments, spiky development load, early inference with unknown demand. | No commitment. Fastest to start. Elastic for bursts. | Highest unit cost at sustained utilization. Capacity for popular accelerators is not guaranteed. |
| Cloud Reserved | Predictable training or inference load you can forecast for a year or more. | Lower unit cost than on-demand. Capacity assurance. Same cloud tooling. | Commitment risk if the workload shrinks or the model strategy changes. |
| Dedicated GPU | Sustained high utilization, strict data residency or isolation requirements, latency-sensitive inference. | Lowest unit cost only at high utilization. Full control of the environment. | Capital and operations burden. Hardware refresh risk. Idle capacity is pure cost. |
| Hybrid | A steady base load plus variable peaks, or regulated data alongside general workloads. | Right-sizes each workload. Keeps burst capacity elastic while the base runs reserved or dedicated. | More moving parts: scheduling, networking, cost allocation and MLOps integration across environments. |
Capabilities
01
Model the development, training, fine-tuning and inference demand of the actual use-case portfolio, with utilization assumptions the finance team can challenge.
02
Cloud on-demand, cloud reserved, dedicated or hybrid, chosen per workload. Where dedicated capacity is justified, NeoIntelli sources it from providers; NeoIntelli does not operate its own GPU data centers.
03
Standardized AI developer environments with model and API access, vector stores, notebooks, experiment tracking and secrets handled properly.
04
Distributed training and fine-tuning pipelines, data loading, checkpointing, artifact management and cost tracking per run.
05
Serving stacks for models and agents, autoscaling, model gateways with routing and fallbacks, latency budgets and capacity reservations where needed.
06
Object and high-throughput storage for datasets and checkpoints, and the network design that keeps GPUs fed and data inside its residency boundary.
07
Infrastructure exposed through the MLOps, LLMOps and AgentOps layer, so deployment, evaluation and observability do not depend on which environment a workload runs in.
08
Cost per training run, per model and per task, utilization dashboards, reservation management and the reviews that stop idle GPUs from becoming a line item nobody owns.
09
Identity and access for compute, isolation for sensitive workloads, secrets and key management, and controls aligned to the client's security policy and residency needs.
Workplace IT, identity and the zero-trust baseline for the center as a whole are covered under Workplace, IT & Security. This page covers the AI compute layer that sits on top of it.
Buyer questions
Not automatically. Most first squads run on cloud on-demand or reserved capacity. Dedicated GPUs make sense when utilization is sustained and high, when data isolation or residency demands it, or when latency-sensitive inference cannot tolerate shared capacity.
Dedicated GPUs are not automatically cheaper than cloud GPUs. Utilization determines the economics. A dedicated cluster that idles half the time is more expensive than the cloud it replaced.
No. NeoIntelli designs, sources, provisions and operates infrastructure across cloud providers and specialist GPU providers. Where dedicated capacity is justified it is sourced and operated for you, not sold from NeoIntelli-owned facilities.
From the workload: model sizes, training and fine-tuning runs, inference volume and latency, and the utilization you can realistically sustain. NeoIntelli does not quote fixed GPU prices on this page because they change by provider, region, accelerator and commitment.
Yes, and it is a common pattern. Residency, latency and cost decide where each stage runs. Hybrid designs keep the base load reserved or dedicated and the bursts elastic.
Compute is exposed through the production operating layer, so a model or agent is deployed, evaluated and observed the same way whether it runs on cloud, reserved or dedicated capacity.
Tell us what the team will develop, train and serve, and where the data may live. We will come back with a capacity plan, a sourcing strategy and the FinOps model to keep it honest.