Production GPU infrastructure for AI factories, private AI, and distributed compute.
setloop.io helps organisations design, validate, build, and operate production GPU platforms for large-scale inference, training, fine-tuning, and secure enterprise AI.
GPU infrastructure fails when it is treated as ordinary cloud infrastructure.
Building an AI platform is not just buying GPUs. The hard problems are scheduling, network topology, storage throughput, cooling, power density, tenant isolation, model-serving latency, training resilience, observability, cost control, and security.
Most organisations discover these problems after procurement, when the architecture is already expensive to change.
This consultancy helps you make the right technical decisions before capital is committed, and then helps your teams deliver the platform into production.
AI infrastructure, from the metal to the model layer.
GPU Infrastructure Strategy
- ·Architecture reviews
- ·Build vs buy analysis
- ·Capacity planning
- ·TCO and cost-per-token modelling
- ·Hardware and vendor evaluation
AI Factory & Datacentre Readiness
- ·Rack density and power assumptions
- ·Cooling and thermal constraints
- ·Network and storage topology
- ·Facility-readiness assessment
- ·Vendor and colo coordination
Cluster & Platform Architecture
- ·Kubernetes GPU platforms
- ·NVIDIA GPU Operator
- ·Slurm, Ray, Kueue, Volcano
- ·Multi-tenant scheduling
- ·Quotas, isolation, chargeback
Inference at Scale
- ·vLLM, SGLang, Triton, TensorRT-LLM
- ·NIM-style model microservices
- ·KV-cache strategy
- ·Autoscaling and routing
- ·Latency, throughput, cost optimisation
Training, Fine-tuning & Post-training
- ·PyTorch distributed training
- ·DDP, FSDP, DiLoCo
- ·Dataset and checkpoint pipelines
- ·Experiment infrastructure
- ·Evaluation and deployment handoff
Private, Sovereign & Secure AI
- ·Local model-serving architecture
- ·Data isolation
- ·PII handling
- ·Agent security controls
- ·Audit logging, policy gates, compliance support
Built from the metal up to the model layer.
setloop.io combines deep systems architecture, distributed engineering, and protocol experience with production AI infrastructure delivery — spanning GPU clusters, datacentre design, model-serving platforms, and secure enterprise AI.
Engagements draw on practitioner experience across production GPU compute networks, distributed model inference, decentralised training infrastructure, multi-tenant security architecture, observability platforms, and Kubernetes-based AI platforms.
- 01Designed and shipped production GPU compute infrastructure across globally distributed hardware
- 02Delivered distributed LLM inference using vLLM and SGLang
- 03Built observability and SRE systems across decentralised node fleets
- 04Operated distributed training workflows using DDP, FSDP, and DiLoCo
- 05Designed multi-tenant isolation for shared AI infrastructure
- 06Led platform and infrastructure engineering teams at scale
- 07Architected Kubernetes-based GPU platforms with NVIDIA GPU Operator
- 08Independent technical evaluation for investors and acquirers
- 09UK & EU delivery for datacentre operators, neo-clouds, and enterprise AI teams
Designed for teams that take infrastructure seriously.
Datacentre operators
- ·Planning GPU-dense infrastructure
- ·Evaluating AI factory strategy
- ·Building customer-facing GPU platforms
Neo-clouds & GPU clouds
- ·Building compute marketplaces
- ·Improving scheduling and utilisation
- ·Designing metering, billing, tenancy
Enterprise AI teams
- ·Deploying private AI
- ·Building internal model platforms
- ·Reducing dependency on public APIs
Investors & CTOs
- ·Technical evaluation
- ·Architecture validation
- ·Infrastructure risk assessment
An AI factory, viewed as a stack.
Every engagement begins by mapping the workload onto the seven layers of an AI factory — from facility through to applications. Each layer has measurable budgets: watts, tokens, dollars, latency, and risk.
Four ways to work together.
AI Infrastructure Assessment
Short diagnostic engagement. Review current architecture, facility assumptions, GPU procurement, network, storage, and operating model.
Risk register, architecture recommendations, technical roadmap.
Reference Architecture & Build Plan
Design a production-ready GPU platform end to end.
Architecture diagrams, component selection, deployment model, security model, observability model, delivery backlog.
Fractional AI Infrastructure Architect
Ongoing technical leadership for CTOs, founders, datacentre operators, and platform teams.
Hands-on architecture, review, vendor management, engineering direction.
Technical Evaluation
Independent assessment of GPU infrastructure companies, AI platform claims, decentralised compute networks, or private AI architectures.
Technical report, risk matrix, leadership questions, investment / procurement recommendation.
Common questions.
No. The consultancy is strongest around NVIDIA-accelerated AI infrastructure, but the architecture process starts from workload, cost, facility, and operating requirements.
Planning serious GPU infrastructure? Validate the architecture before committing capital.
For enterprises, datacentre operators, AI startups, GPU clouds, and CTOs building production AI platforms.
or write sales@setloop.io