Infrastructure bought before workload sizing
Model size, context, concurrency, availability, growth, and facility constraints determine the capacity that is useful.
Consulting / AI Lab Infrastructure
Your AI lab, commissioned for real workloads.
Gyde turns a set of approved workloads into an infrastructure plan, a commissioned GPU environment, hosted model endpoints, and an operating model your team can own.
AI lab commissioning plan
Representative scope
Workloads →︎ Capacity →︎ Platform →︎ Operations
Hardware, model, serving, security, and support decisions are made from representative demand and documented acceptance criteria.
Capacity specification
License review
Private endpoints
Named operators
01On-premises, colocation, or private cloud
02Open-weight and open-source models
03Handover or managed operations
Why this layer matters
GPU capacity is one part of an internal model service.
A usable AI lab also needs workload sizing, power and network readiness, cluster software, model licensing, serving APIs, identity, telemetry, release procedures, and an accountable operating team.
A cluster without a service boundary
Teams need approved models, stable endpoints, access controls, quotas, evaluation gates, and support procedures before applications can depend on the lab.
Unassigned platform ownership
Drivers, orchestration, model releases, security updates, incidents, utilization, and capacity planning need named owners and operating routines.
What we deliver
One lab programme across procurement, platform, and operations.
01
Architecture & procurement
Translate named workloads and enterprise constraints into the facility, accelerator, network, storage, and support requirements used for vendor evaluation.
- Workload and demand profile
- Buy, lease, cloud, and hybrid comparison
- Procurement specification and offer review
02
Cluster & model platform
Commission the selected capacity and build the software path from model artifacts to secured, application-ready endpoints.
- GPU drivers, orchestration, storage, and registry
- Model evaluation, packaging, and serving
- Identity, gateway, quotas, and deployment automation
03
Acceptance & operations
Test the lab under representative traffic, instrument the operating path, and establish ownership for releases, incidents, cost, and growth.
- Load, failure, security, and recovery tests
- Service, GPU, and cost dashboards
- Runbooks, training, and support model
Technical commissioning
Every layer has a configuration record and an acceptance test.
The final implementation matrix records component versions, dependencies, owners, test evidence, and rollback procedures for the commissioned environment.
LayerEngineering decisionsAcceptance evidence
01
Facility & nodes
Power, cooling, rack layout, firmware, BMC access, CPU and memory ratio, PCIe, NUMA, NVLink or NVSwitch topology
Facility checklist, burn-in results, topology record, hardware health baseline
02
Network & storage
Management, service and storage planes; Ethernet or InfiniBand; RDMA where justified; object storage, shared storage and local NVMe cache
Bandwidth, latency, collective communication, storage throughput and recovery tests
03
Cluster software
Linux baseline, GPU drivers, CUDA or ROCm, container runtime, Kubernetes or Slurm, device plugins, node labels and GPU operators
Version matrix, repeatable bootstrap, scheduling test and node replacement procedure
04
Models & serving
License approval, artifact registry, runtime selection, precision, tensor or pipeline parallelism, batching, cache and replica shape
Model register, quality comparison, load profile, release and rollback test
05
Access & traffic
Private DNS and endpoints, API gateway, service identity, TLS, secrets, network policy, quotas, admission control and tenant boundaries
Access review, isolation checks, rate-limit test and request audit trail
06
Telemetry & operations
GPU and service metrics, logs and traces, alert thresholds, incident flow, patching, model upgrades, capacity and cost allocation
Dashboards, alert drill, failure recovery record, runbooks and named ownership
Delivery record
The lab arrives with evidence and operating ownership.
Each procurement and production decision is recorded so the customer can review the basis, operate the environment, and plan the next capacity change.
- Workloads
- Demand profile and service objectives
- Capacity
- Architecture, bill of materials, and vendor comparison
- Models
- License, quality, and deployment register
- Platform
- Configured cluster, endpoints, and release pipeline
- Acceptance
- Load, failure, security, and recovery results
- Operations
- Dashboards, runbooks, ownership, and support scope
01
02
03
04
05
06
The engagement
Four decisions move the lab from demand to service.
The customer contracts directly with hardware, colocation, and cloud vendors. Gyde owns the technical requirements, comparison, commissioning, and acceptance work within the agreed scope.
1
Assess
Size the demand
Profile the workloads, data boundary, traffic, quality thresholds, availability, growth, facilities, skills, and budget.
2
Source
Select the capacity
Compare owned, leased, cloud, and hybrid options; prepare the specification; review offers; and define acceptance tests.
3
Commission
Build the model service
Bring up the cluster, deploy approved models, expose secured endpoints, connect applications, and instrument the full serving path.
4
Operate
Prove and transfer
Run acceptance tests, establish release and incident procedures, train operators, and begin the agreed support or handover period.
What you leave with
A working internal model service with known limits.
The final record states what the lab can serve, how it performed, who operates it, and when capacity or architecture should change.
Commissioned infrastructure
Accepted GPU, network, storage, orchestration, registry, and monitoring components in the selected customer or approved provider environment.
Hosted model endpoints
Selected open-weight or open-source models exposed through secured application contracts with evaluations and release controls.
Operating capability
Named ownership, dashboards, runbooks, support procedures, cost visibility, and expansion thresholds for the lab.
Typical building blocks
NVIDIA or AMD GPUsKubernetes or SlurmGPU operators and device pluginsNCCL or suitable collectivesModel registriesvLLM, TGI, Triton, or NIMDCGM, Prometheus, and GrafanaObject and NVMe storage
Specialist engineering
Private GPU Inference
Use the focused inference engagement when capacity already exists and the immediate problem is model, runtime, hardware, performance, or serving economics.
Questions
Before we begin.
Does Gyde sell or resell GPU hardware?
No. The customer contracts directly with the selected hardware, colocation, cloud, or capacity vendor. Gyde prepares technical requirements, compares suitable offers, supports procurement decisions, commissions the environment, and runs the agreed acceptance tests.
Can the lab run in our data centre?
Yes, when power, cooling, rack, network, storage, security, and support prerequisites can meet the selected workload. We assess those dependencies before the purchase specification is approved.
Can this use cloud or leased GPU capacity?
Yes. The architecture can use a private cloud account, specialist capacity provider, colocation environment, customer-owned hardware, or a documented hybrid of these options.
Which models can you host?
Model selection begins with task quality, license terms, hardware fit, context needs, language coverage, security boundary, and operating economics. The model register records those decisions for each approved workload.
Why does the page say open-weight and open-source?
Model licenses differ. Some permit broad open-source use, while others publish weights under community, research, or commercial terms. The engagement records the applicable license and customer approval before deployment.
Will Gyde operate the lab after launch?
The engagement can end with customer-team handover, include a transition period, or continue under an agreed operating scope. Release, monitoring, incident, upgrade, security, and capacity responsibilities are documented before go-live.
When should we keep using managed model APIs?
Managed APIs can remain the better choice for small, volatile, or specialist workloads. The assessment compares security, quality, latency, availability, operating effort, and total cost before recommending dedicated capacity.
Can you assess GPUs we already own?
Yes. We assess the accelerator memory, server and network topology, storage, drivers, orchestration, current utilization, and target workloads before deciding what the estate can support.
Bring a defined business constraint
Bring the workloads before the purchase order.
We will turn the demand, security boundary, operating constraints, and budget into an AI lab assessment and a reviewable commissioning path.
Plan the AI lab