We don't operate GPUs. We operate GPU utilization.
GPU Cluster, AI Storage, high-speed network and power & cooling — GIIP designs, operates and optimizes the full AI infrastructure stack, not just the servers in it. Installing GPUs is not the same as running an AI service on them.
AIDC Design · GPU Infrastructure · AI Storage & Fabric · Data Center Operations · GIIP AI Operations
We don't operate GPUs. We operate GPU utilization.
A rack full of GPUs is not the same thing as a running AI service.
GPU utilization is decided by Compute Fabric, Storage, Network, Power, Cooling, Scheduling, Monitoring, Security and incident response working together as one system — not by the GPU alone.
GPUs sit idle for reasons that have nothing to do with the GPU
Slow data delivery, storage throughput limits, network congestion and inefficient job queues all show up as idle GPUs.
Power and cooling limits show up as throttling
A cluster designed without enough power and cooling headroom throttles under real load, no matter how capable the GPUs are.
GPU vendors, data centers and internal teams each own only part of the stack
When something breaks, it is often unclear who is responsible for diagnosing it end to end.
General server operations does not see GPU-specific failure modes
ECC errors, XID events, NVLink faults and fabric degradation live below what ordinary Linux monitoring can see.
One operations layer across the whole AI infrastructure stack.
From AIDC design through GPU infrastructure, AI storage and fabric, data-center operations and GIIP AI operations — five axes that together keep GPUs running at high utilization instead of sitting idle.
Designed against the workload, not the server count
AIDC Design sizes compute, storage, network and power/cooling against the actual AI workload you plan to run.
One lifecycle from build to operations
PLAN → BUILD → OPERATE → OPTIMIZE → EXPAND — GIIP stays involved across the whole lifecycle instead of handing off after installation.
AI-analysed monitoring, not just dashboards
GIIP AI correlates logs, metrics and change history to surface anomalies, likely causes and recommended actions — then tracks whether the action worked.
Approval-gated, always
Safe, routine work is automated; high-risk work always waits for your approval before it runs.
What we operate
GPU servers, AI servers and DGX/HGX-class systems, the GPU clusters built from them, and the data-center infrastructure that houses them.
Targets
- GPU servers and AI servers (single node to multi-node clusters)
- DGX/HGX-class systems and OEM GPU servers
- GPU clusters spanning multiple racks and multiple sites
- The data-center infrastructure that hosts them
Infrastructure
- Servers & compute nodes
- Storage (shared storage, local cache, backup)
- L2/L3 network and fabric (NVLink/NVSwitch, InfiniBand/Ethernet)
- Uplink & external circuits
- Racks, PDU and cabling
- Power capacity and distribution
- Cooling and the surrounding operating environment
Installing GPUs is not the same as running an AI service on them. Every layer above has to work together, continuously, for the GPUs to actually stay busy.
What we do
From the initial build review through day-to-day operations: state monitoring, incident response, performance and capacity management, cost optimization, operational automation and regular reporting.
- 1
Introduction & build review — sizing the compute, storage, network and power/cooling before anything is racked
- 2
State monitoring — continuous visibility across GPU, fabric, storage, network, power and cooling
- 3
Incident response — triage, root-cause analysis and recovery support when something goes wrong
- 4
Performance & capacity management — keeping GPU utilization, throughput and headroom visible and actionable
- 5
Cost optimization — surfacing idle capacity, workload placement and spend that can be reduced
- 6
Operational automation — turning repeatable operational work into safe, auditable automation
- 7
Regular reporting — a recurring view of utilization, incidents and cost for stakeholders
GIIP does not replace BMC/IPMI, DCGM, Prometheus or Grafana. It sits on top of them and adds AI analysis, an approval-gated action trail, and reporting — the same operating principle as our GPU server operations service.
How GIIP AI operates it
Collecting logs and metrics is only the first step. GIIP AI turns that raw signal into a judgement call an operator can act on — and then tracks whether the action actually worked.
- 1
Collect logs, metrics and change history from every layer — GPU, fabric, storage, network, power and cooling.
- 2
AI correlates the signals and flags anomalies that a single-threshold alert would miss.
- 3
AI proposes a likely root cause and a candidate response, ranked by confidence.
- 4
The operator reviews, approves or adjusts the proposed action — high-risk work is never executed unconditionally.
- 5
GIIP tracks the outcome of the executed action and feeds it back into the next analysis.
The goal is not full autonomy. It is making sure a human operator always has the context, the recommendation and the audit trail to act quickly and safely.
Five operating axes
AIDC Operations is organized around five axes that together cover the full stack from design to day-to-day AI operations.
| Axis | What it covers |
|---|---|
| AIDC Design | Designed against the full AI workload — not just a GPU server count. |
| GPU Infrastructure | Compute operations across HGX/DGX/B200/H200-class systems and OEM equivalents. |
| AI Storage & Fabric | Design and operation of NDR/400G fabric, NVMe, shared storage and local cache. |
| Data Center Operations | Power, cooling, rack, PDU, network and capacity management. |
| GIIP AI Operations | Monitoring, incident analysis, response support, cost and performance optimization. |
From build to operations: one lifecycle
Building AI infrastructure and operating it are not separate projects. GIIP treats both as one continuous lifecycle.
PLAN
- Capacity
- Power
- Cooling
- Network
BUILD
- Rack
- GPU
- Storage
- Fabric
OPERATE
- Monitoring
- Scheduling
- Incident response
OPTIMIZE
- GPU utilization
- Cost
- Performance
EXPAND
- Scale-out
- New GPU
- Additional rack
Decisions made at PLAN and BUILD determine how much can realistically be optimized later — which is why GIIP stays involved across the whole lifecycle instead of handing off after installation.
General server operations vs. GPU / AIDC operations
A GPU cluster fails in ways that ordinary Linux server operations were never designed to see.
| General server operations | GPU / AIDC operations | |
|---|---|---|
| What is monitored | CPU, memory, disk, OS-level services | The above, plus GPU utilization, ECC/XID faults, NVLink/NVSwitch fabric and BMC state |
| Failure modes | Process crash, resource exhaustion, disk failure | GPU throttling, fabric degradation, power/cooling-driven failures, silent data corruption |
| Capacity planning | Server count and disk headroom | GPU utilization, power budget, cooling headroom and rack density together |
| Who needs to read the signal | A generalist ops engineer | Someone who can interpret GPU-specific telemetry — which is exactly what GIIP AI is built to support |
What this gives you
- Less dependence on scarce, specialized GPU and data-center operations talent
- Faster incident response, from detection to root cause to recovery
- Standardized operating procedures instead of tribal knowledge
- Visibility into resource, power and cost that was previously invisible
AI Data Center business (in preparation)
GIIP is preparing a dedicated AI data center business alongside its current AIDC Operations service. This is a future direction we are actively working toward — not a service we operate or sell today.
None of the items above are available today. We are not currently operating our own data center or reselling colocation capacity. If you are evaluating a future data-center partner, we welcome an early conversation — but please treat this section as a roadmap, not a live offering.
What this covers
5 operating axes
AIDC Design → GIIP AI Operations
PLAN → EXPAND
One lifecycle, not separate build and operate projects
BMC/IPMI · DCGM · Prometheus · Grafana
Integrates with the monitoring stack you already run
AI Data Center
A future business we are preparing — clearly marked as not yet available
Frequently asked questions
How is AIDC Operations different from AI Infrastructure Operations or GPU server operations?
AIDC Operations is the umbrella that connects them: AI Infrastructure Operations covers provisioning and cloud automation, GPU server operations covers DGX/HGX monitoring and incident response, and database performance tuning covers the data layer. AIDC Operations organizes all of this around five axes and one PLAN-to-EXPAND lifecycle so the full stack is designed and run as one system.
Do you sell or officially represent any GPU vendor?
No. GIIP operates GPU and AI infrastructure regardless of vendor. We do not claim an official partnership or reseller status with any specific manufacturer.
Is the AI Data Center business available today?
No, it is in preparation. GIIP does not currently operate its own data center or resell colocation capacity. See the dedicated section below for what is planned versus what is available now.
What does "GIIP AI Operations" actually do, beyond collecting metrics?
Collecting logs and metrics is the first step. GIIP AI correlates that data to identify anomalies, propose a likely root cause and a recommended response, and then tracks whether the executed action resolved the issue — with high-risk actions always gated on your approval.
Can we start with just one axis, like AI Storage & Fabric or GPU Infrastructure?
Yes. Most engagements start from whichever axis is causing the most pain today — often GPU Infrastructure or monitoring — and expand from there as the relationship develops.
Talk to us about operating your GPU / AI infrastructure.
Tell us what you run — GPU servers, DGX/HGX systems, or a cluster spread across racks — and we will show you the monitoring scope, the AI operation flow, and where the responsibility lines sit.