AI Infrastructure & GPU Engineering

AI infrastructure is where ambition meets physics: power, cooling, interconnect bandwidth and scheduling determine what your organization can actually train and serve. Novera Intelligence engineers GPU environments that are architecturally sound from day one — sized for your workloads, networked for the traffic they generate, and operable by your team.

We design and build everything from single-node development servers to multi-rack AI factories, spanning NVIDIA and AMD GPU environments, with the storage, networking, orchestration and benchmarking discipline that separates production infrastructure from a hardware pile.

The Challenges We Solve

• Clusters that underperform their paper specs

  GPU utilization far below nameplate is the norm when networking, storage or scheduling is misconfigured. We profile, benchmark and tune until the cluster delivers its rated capability.

• Scaling from pilot to AI factory

  A handful of GPUs in a lab is easy; dozens of nodes with fabric-level networking, power coordination and multi-tenancy is an engineering program. We design for the scale you are heading toward.

• Scheduling and contention

  Competing teams, mixed training and inference workloads, and shared storage create contention that wastes capacity. We implement GPU scheduling, quota and multi-tenancy models that make allocation deliberate.

• Power, cooling and facilities alignment

  High-density GPU racks change facility requirements. We coordinate with facilities stakeholders on power, cooling and floor planning before procurement, not after delivery.

What We Deliver

• AI factory design

  End-to-end architecture for dedicated AI facilities: compute density, fabric topology, storage tiering, power and cooling coordination and lifecycle planning.

• GPU cluster architecture

  Node, network and storage topology design for training and inference clusters, with sizing models grounded in your workload profiles.

• NVIDIA & AMD GPU environments

  Design and deployment across current-generation GPU families, with driver, runtime and library stack engineering for each.

• High-performance networking

  InfiniBand and high-performance Ethernet fabrics, RDMA configuration, topology-aware job placement and network performance validation.

• Storage architecture

  Parallel and object storage sized for training throughput and checkpointing, tiered to cost and latency requirements.

• Distributed computing & orchestration

  Kubernetes GPU operators, Slurm clusters, and hybrid scheduling designs that fit research and production workloads in one estate.

• GPU scheduling & multi-tenancy

  Partitioning, quotas, priority queues and fair-share policies so teams share capacity predictably.

• Benchmarking & validation

  Pre-production benchmarking of compute, interconnect and storage paths, and acceptance testing against your workload profiles.

• Infrastructure lifecycle planning

  Refresh strategy, capacity forecasting and expansion design that keeps the estate aligned to model and demand growth.

Architecture in Layers

A reference frame for AI factories

1. Accelerated compute

   GPU node design, sizing, and driver/runtime stack selection matched to training and inference profiles.

2. Fabric

   InfiniBand or high-performance Ethernet, RDMA, and topology-aware placement for collective-heavy traffic.

3. Storage

   High-throughput parallel and object tiers sized for streaming datasets and checkpoint storms.

4. Scheduling

   Slurm and Kubernetes scheduling, GPU partitioning, quota and fair-share across tenants.

5. Facilities

   Power, cooling, density and expansion path coordinated with your facilities and procurement teams.

How We Engage

Delivered through a disciplined lifecycle that carries the initiative from assessment to dependable, optimized operation.

1. 01

   Workload profiling

   Characterize training and inference workloads: model classes, batch shapes, IO patterns, concurrency and growth.

2. 02

   Architecture design

   Cluster topology, network fabric, storage tiering and scheduling model, with sizing and cost options.

3. 03

   Facilities coordination

   Power, cooling and density alignment with facilities stakeholders; procurement technical specifications.

4. 04

   Build & integrate

   Deployment of the compute, network, storage and scheduling stack with configuration-as-code.

5. 05

   Benchmark & handover

   Acceptance benchmarking, performance validation, documentation and operational training.

Deliverables You Can Expect

• Cluster architecture and topology design
• Network fabric and RDMA configuration standards
• Storage architecture and capacity model
• Scheduling and multi-tenancy policy design
• Benchmark and acceptance test reports
• Facilities requirements for power and cooling
• Runbooks and operational documentation

Use Cases

• Dedicated AI factory

  An organization building its own GPU facility for training and inference needs an architecture that survives scale: fabric, storage, scheduling and expansion designed together.

• Research computing modernization

  Universities and research institutions consolidating departmental clusters into a scheduled, shared GPU environment with fair allocation.

• GPU cloud operator launch

  Providers standing up GPU cloud capacity need cluster engineering, tenancy isolation and operational tooling from day one.