Consultancy services

Building for companies their GPU workloads.

setloop.io helps teams make better architecture, product, platform, and procurement decisions before expensive GPU infrastructure choices are locked in.

Advisory areas

A simpler way to plan GPU infrastructure.

The work is focused on decisions that affect cost, reliability, security, performance, and commercial viability.

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GPU Workload Architecture

·Training, fine-tuning, inference, RAG, agents, and batch workloads
·GPU class, topology, memory, network, and storage requirements
·Latency, throughput, resilience, and cost targets

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GPU Cloud Product Advisory

·GPU rentals, deployments, private clusters, and training APIs
·Secure multi-tenancy, customer access, billing, and usage attribution
·Commercial readiness for datacentre operators and GPU cloud providers

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Private AI Infrastructure

·On-prem, private cloud, sovereign cloud, and hybrid deployment patterns
·Tenant isolation, policy controls, audit logs, and data residency
·Secure RAG, internal agent platforms, and private model serving

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Technical Evaluation

·Vendor, startup, procurement, and investment architecture reviews
·Scalability, utilisation, unit economics, security, and operational risk
·Risk register, maturity score, and go / conditional / no-go recommendation

Practice detail

The full consultancy surface.

The site is simpler, but the service offer is intentionally broad: from GPU strategy and AI infrastructure architecture through to private AI, inference, training, security, SRE, FinOps, and technical evaluation.

/00

Enterprise AI Strategy & Transformation

End-to-end AI implementation services for enterprises transitioning from prototypes to production.

Planning

·AI transformation roadmap
·Use case prioritization
·AI readiness assessment
·Vendor evaluation

Value & FinOps

·AI ROI consulting
·TCO modeling
·Cost-per-outcome alignment
·Build vs. buy analysis

/01

GPU Infrastructure & Architecture

Scalable AI compute architecture and hybrid cloud AI infrastructure design.

Cluster design

·Scalable compute architecture
·Hybrid cloud AI infrastructure
·GPU orchestration for AI workloads
·Network topology

Workload analysis

·Inference
·Fine-tuning
·Training
·RAG
·Agents
·Batch
·Simulation

Hardware selection

·A100 / H100 / H200
·B200 / GB200-class
·DGX / HGX
·Cloud, colo, hybrid

Economics

·Build vs buy
·CapEx vs OpEx
·TCO modelling
·Cost per token
·Utilisation targets

Procurement

·Vendor evaluation
·Lead-time risk
·Allocation strategy
·Contract assumptions

/02

AI Infrastructure Architecture

Reference architecture across facility, hardware, fabric, orchestration, runtime, platform, and applications.

Facility assumptions

·Rack density
·Power envelope
·Cooling model
·Colocation fit

Cluster design

·Kubernetes GPU
·Slurm
·Ray
·Kueue
·Volcano
·Multi-tenant queues

Network & storage

·InfiniBand
·RoCE
·Spectrum-X
·NVLink domains
·Checkpointing
·Object storage

Delivery plan

·Component selection
·Security model
·Observability model
·Delivery backlog

/03

Private & Sovereign AI

Production AI for organisations that cannot send sensitive data to public APIs.

Deployment patterns

·On-prem
·Private cloud
·Sovereign cloud
·Hybrid
·Restricted-network

Private model platforms

·Local LLM serving
·Secure RAG
·Private vector stores
·Internal agent platforms

Data controls

·Data residency
·PII handling
·Audit trails
·Retention policy
·Access-scoped retrieval

Governance

·Tenant isolation
·Policy gates
·Tool-call controls
·SOC 2 / ISO 27001 / GDPR mapping

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Inference Platform Engineering

Production inference for LLMs, multimodal models, and agent backends.

Runtimes

·vLLM
·SGLang
·Triton
·TensorRT-LLM
·NIM-style services

Performance

·KV-cache strategy
·Prefix caching
·Continuous batching
·Speculative decoding
·Quantisation

Routing & scale

·Model routing
·Autoscaling
·Multi-model serving
·GPU memory planning

Operations

·API gateway
·Rate limiting
·Auth
·Chargeback
·Latency benchmarking

/05

Training & Fine-tuning

Distributed training workflows that survive long runs, node failures, and network disruption.

Frameworks

·PyTorch
·DDP
·FSDP
·DiLoCo
·LoRA
·QLoRA

Pipelines

·Dataset pipelines
·Checkpoint strategy
·Fault tolerance
·Distributed dataloaders

Evaluation

·Experiment tracking
·Evaluation harnesses
·Fine-tuning workflows
·Deployment handoff

Utilisation

·GPU utilisation
·Queue depth
·Throughput tuning
·Training observability

/06

Distributed GPU Networks

Advisory for decentralised GPU compute, GPU marketplaces, and multi-provider workload platforms.

Scheduling

·Distributed scheduling
·Provider selection
·Latency-aware placement
·Fault tolerance

Network

·P2P architecture
·Secure mesh
·Worker connectivity
·Gateway policy

Trust & economics

·Verification
·Metering
·Billing
·Reputation
·Provider scoring

Workloads

·Distributed inference
·Distributed training
·Benchmarking
·Capacity markets

/07

Security & Responsible AI Implementation

Securing model-serving systems and establishing an enterprise AI governance framework.

Model & agent

·Prompt-injection controls
·Tool-call governance
·Agent policy gates
·Model access control

Platform

·Runtime isolation
·Container security
·Supply-chain security
·Secrets management

Data

·PII scanning
·Audit logs
·Tenant boundaries
·Encryption assumptions

Compliance

·SOC 2 mapping
·ISO 27001 mapping
·GDPR mapping
·SIEM integration

/08

SRE, Observability & FinOps

Operating GPU platforms with measurable SLOs and visible unit economics.

Signals

·GPU utilisation
·Queue depth
·P50 / P95 / P99 latency
·Tokens per second
·Cost per token

Tooling

·Prometheus
·Grafana
·Datadog
·OpenSearch
·NVIDIA DCGM

Operations

·SLOs
·Error budgets
·Runbooks
·Incident response
·Alerting

FinOps

·Capacity forecasting
·Chargeback
·Tenant metering
·Cost attribution

/09

Technical Evaluation

Independent assessment for investors, acquirers, enterprise buyers, and leadership teams.

Architecture

·Architecture credibility
·Scalability
·Failure modes
·Roadmap feasibility

Economics

·GPU utilisation assumptions
·Unit economics
·Cost-per-token claims
·Vendor lock-in

Operations

·Platform maturity
·Observability
·Security model
·Team capability

Deliverables

·Technical report
·Risk register
·Maturity score
·Go / conditional / no-go recommendation

Compliance & Governance

Enterprise AI governance framework.

We provide AI regulatory compliance consulting to ensure your platform meets the rigorous legal frameworks governing responsible AI implementation in Europe and the UK.

EU AI Act ↗

The world's first comprehensive legal framework for AI, ensuring systems used in the EU are safe, transparent, traceable, and non-discriminatory.

GDPR (UK & EU) ↗

The General Data Protection Regulation governs how personal data is collected and processed by IT and AI systems within the EU and UK.

EU Data Act ↗

Maximizes the value of data by regulating who can access and create value from data generated in the EU.

EU Digital Sovereignty ↗

Ensures Europe's control over its digital destiny, prioritizing sovereign cloud and secure, localized AI deployments.

EU DORA ↗

The Digital Operational Resilience Act ensures financial entities can withstand and respond to severe ICT-related disruptions.

PCI-DSS ↗

The Payment Card Industry Data Security Standard mandates a secure environment for any entity processing cardholder data.

UK FCA ↗

The Financial Conduct Authority regulates financial markets in the UK, overseeing secure, resilient, and fair use of technology.

Engagements

How companies work with setloop.io.

/01

GPU Infrastructure Review

A short assessment of workload requirements, architecture risks, platform gaps, and next decisions.

/02

Reference Architecture

A concrete build plan covering components, tenancy, scheduling, storage, security, observability, and delivery phases.

/03

Fractional GPU Architect

Ongoing advisory and technical leadership for founders, CTOs, platform teams, and operators.

/04

Independent Evaluation

A written technical report for investors, acquirers, buyers, or leadership teams before committing capital.

Product

Need to launch a GPU cloud?

GPU Cloud is the product offer for companies that want to turn GPU capacity into a secure, governed, revenue-ready cloud platform.

The product architecture covers deployments, rentals, private clusters, distributed workloads, training APIs, secure tenancy, usage billing, cost-aware autoscaling, mesh networking, and recoverable storage.

View GPU Cloud Product Book a Review