Accelerated compute
Size GPU and CPU capacity from measured training, tuning, simulation, and inference demand. Utilization and growth assumptions matter more than headline hardware specifications.
AI Infrastructure & AI Factories
The production foundation behind enterprise AI.
GPU capacity is only one part of a production AI environment. SVCG designs the compute, network, storage, model-serving, data, and operating layers around the workloads the organization needs to run.
What an AI factory includes
An AI factory is the production environment used to build and run AI workloads repeatedly. It combines accelerated compute with the network, storage, orchestration, security, model serving, observability, and operating processes required to keep that compute useful. We size the architecture from workload measurements and operating constraints, not from a hardware wish list.
Architecture
Every layer has to work as one system.
AI networking
Design east-west bandwidth, topology, fabric choices, congestion controls, segmentation, and external connectivity around distributed workloads and operational boundaries.
Data and model storage
Match object, file, block, cache, checkpoint, and dataset storage to throughput, metadata, retention, lineage, and recovery requirements.
Workload orchestration
Coordinate scheduling, quotas, isolation, environments, dependencies, retries, and lifecycle operations across shared AI infrastructure.
Model serving
Build reliable inference paths with routing, batching, autoscaling, versioning, latency objectives, fallback behavior, and model or vendor flexibility.
Observability and FinOps
Track utilization, queue time, latency, failures, capacity, energy, and unit cost. Capacity decisions should be supported by operating data.
Engagement path
From workload inventory to operating platform.
01
Workload map
Define models, data movement, latency, throughput, security, sovereignty, and growth assumptions.
02
Architecture
Compare cloud, private, colocation, on-premises, managed, and hybrid deployment patterns.
03
Proof
Benchmark representative workloads and validate bottlenecks, operations, and economics.
04
Production
Stand up governance, observability, runbooks, lifecycle management, and capacity planning.
Agents and platforms
Infrastructure that supports action.
An inference endpoint is only one dependency for an enterprise agent. Agents also need secure company context, governed tool execution, durable workflow state, traces, evaluations, retries, human approvals, and monitoring. These services should be part of the platform rather than rebuilt for every agent.
Frequently asked questions
What is an enterprise AI factory?
An enterprise AI factory is the production environment used to build and run AI workloads repeatedly. It combines accelerated compute with the network, storage, orchestration, security, model serving, observability, and operating processes required to keep that compute useful.
Does every organization need to build its own GPU cluster?
Usually not. Public cloud, private cloud, colocation, on-premises systems, and managed inference each fit different workloads. The choice should follow measured demand, data location, latency, utilization, operating capability, and cost.
How does AI infrastructure support enterprise agents?
Agents need reliable model endpoints, governed access to data and tools, durable workflow state, traces, evaluations, monitoring, and predictable capacity. Those services belong in the platform rather than being rebuilt inside every agent.
Start with the workload, not the hardware.
Email info@svc.group with the models, throughput, latency, data location, deployment constraints, and expected growth. We will help turn those inputs into an architecture and validation plan.