AI Factory Setup Consulting

What Is an AI Factory?

The term "AI factory" describes infrastructure purpose-built for the continuous production of AI models. Unlike traditional data centers optimized for web serving or databases, an AI factory is optimized for a single objective: converting raw data and compute into trained models as efficiently as possible. This requires a fundamentally different approach to architecture, one where every layer of the stack is designed around the specific demands of GPU-accelerated workloads.

An AI factory encompasses five critical layers: the compute layer providing GPU-dense servers with high-bandwidth interconnects, the network layer providing RDMA fabric for distributed training, the storage layer delivering the I/O throughput to keep GPUs fed, the orchestration layer managing workload scheduling and resource allocation, and the operations layer ensuring reliability, monitoring, and continuous optimization. Getting any single layer wrong creates a bottleneck that limits the entire system. A cluster of H100 GPUs connected by a slow network is an expensive waste. The fastest network in the world is useless if the storage cannot feed data fast enough. And without proper orchestration, even perfectly configured hardware sits idle while teams wait for access.

The Five Layers of an AI Factory

Our Approach

Hardware Selection and Sizing

Choosing the right hardware is the first and most consequential decision in building an AI factory. We help you navigate the complex landscape of GPU server options, networking equipment, and storage systems. For GPU servers, we evaluate the tradeoffs between DGX systems (turnkey but expensive), HGX reference designs from OEMs (lower cost, more flexibility), and custom builds (maximum flexibility, highest operational burden). We consider your workload requirements: large language model training demands maximum GPU memory and inter-node bandwidth, while inference serving may benefit from a larger number of smaller GPUs. We also factor in power and cooling constraints, which increasingly drive architecture decisions as GPU power consumption continues to rise. A single H100 SXM draws 700W. A rack of eight DGX H100 systems consumes over 80 kW, requiring liquid cooling in most environments.

Network Architecture

The network is the single most impactful infrastructure decision after GPU selection. We design GPU cluster networks using rail-optimized topologies where each GPU in a node connects to a dedicated network rail through its own NIC, providing maximum per-GPU bandwidth and eliminating contention. For InfiniBand deployments, we design fat-tree or dragonfly topologies with NVIDIA Quantum switches, configure the subnet manager for optimal routing, and enable adaptive routing for load balancing. For RoCE deployments, we design leaf-spine fabrics with proper oversubscription ratios, configure lossless Ethernet with PFC and ECN, and implement ECMP for multi-path load balancing. We always separate compute traffic (RDMA for training) from storage traffic (data loading) on independent network fabrics to prevent interference.

Orchestration: Kubernetes vs. Slurm

The choice between Kubernetes and Slurm for GPU workload orchestration depends on your team's existing expertise, workload mix, and operational requirements. Slurm has been the standard scheduler in HPC and AI research for years. It excels at batch job scheduling, has native support for MPI and multi-node jobs, and is well-understood by the research community. Kubernetes has become the enterprise standard for container orchestration and is increasingly adopted for GPU workloads. It offers a richer ecosystem of tooling, better support for mixed workloads (training plus inference plus data pipelines), and more mature multi-tenancy capabilities.

We implement either or both, depending on your needs. For Kubernetes GPU clusters, we deploy the full NVIDIA GPU stack including GPU Operator, Network Operator, and advanced schedulers like KAI Scheduler for topology-aware GPU scheduling. For Slurm clusters, we configure Slurm with gres/gpu support, Pyxis for container integration, and Enroot for rootless container execution. Many organizations run both: Kubernetes for inference serving and CI/CD, with Slurm for large-scale training jobs.

Monitoring and Operations

An AI factory requires purpose-built monitoring that goes far beyond standard infrastructure observability. We deploy DCGM-based GPU monitoring with Prometheus and Grafana, implement XID error detection and automated GPU fault recovery, build capacity planning dashboards that track utilization trends and inform procurement decisions, create operational runbooks for common failure scenarios, and set up cost tracking and chargeback systems for multi-tenant environments. We also establish SLOs for GPU infrastructure availability and training job success rates, giving you quantifiable targets for infrastructure reliability.

Cost Planning and TCO Analysis

Building an AI factory is a significant capital investment. We provide detailed TCO (Total Cost of Ownership) analysis that covers hardware procurement costs including GPUs, networking, storage, and racks, facility costs including power, cooling, and space, operational costs including staffing, maintenance contracts, and spare inventory, and comparison against cloud GPU pricing for your specific workload patterns. This analysis helps you make an informed build-vs-buy decision and, if you decide to build, ensures the investment is sized correctly for your needs.

Technologies We Work With

Related Resources

• GPU-to-GPU Communication Across Nodes
• Network Bottlenecks in Distributed Training
• GPU Power Management
• Case Study: 8.5x Faster Training with RDMA
• Distributed Training Optimization Service
• GPU Networking & RDMA Consulting
• GPU Kubernetes Consulting
• GPU Monitoring & Observability

Planning an AI Factory?

Whether you are building from scratch or scaling an existing cluster into a full AI factory, we bring the expertise to get every layer right. Let us help you avoid the expensive mistakes.