GPU Utilization & AI Capacity Analyzer
rack2cloud — AI Infrastructure
Accelerator Capacity Analysis
AI Infrastructure — Cloud Cost Governance Toolkit

GPU Utilization
& AI Capacity Analyzer

Your dashboard shows utilization. This tool surfaces the number that matters: Effective GPU Yield — provisioned accelerator capacity discounted by allocation drift, fragmentation, and scheduling failure.

✓ Input-driven. No telemetry required.
✓ Capacity Aligned is a valid output.
✓ Runs entirely in your browser.
Layer 01
Yield Analysis
Layer 02
Allocation Reality
Layer 03
Fragmentation & Scheduling
Layer 04
Commitment Structure
01 Fleet Inventory
▶
All GPUs provisioned — owned, reserved, on-demand combined
Dominant procurement structure — drives Reservation Overhang weighting
02 Acquisition & Commitment
▶
Combined reserved + on-demand / spot cost per month
20%
Expected annual GPU expansion rate — drives Yield Recovery Horizon
Blended rate across procurement types — used for Economic Density Loss
2.5 Workload Profile
▶
Drives archetype interpretation, Yield Loss Composition weighting, and cross-tool routing
03 Capacity Reality Signals
▶
62%
What your monitoring dashboard reports — the number this tool deconstructs
80%
Fraction of provisioned GPUs actively allocated to any workload or reservation
Persistent queue alongside allocated-but-idle capacity is the Queue–Idle Paradox signal
04 Fragmentation & Scheduling
▶
Scheduling maturity is frequently the root cause of apparent capacity shortage
15%
Allocated to a job or namespace but consuming no meaningful compute work
25%
Workloads that could run on a GPU slice but are allocated a full card
05 Workload Mix
▶
50%
30%
Drives Inference Persistence Signal and cross-tool routing to CREE
GPU Capacity Analysis
Block A — Recognition
GPU Yield Efficiency Signal
Phantom Scarcity Detection
Capacity Illusion Index
Block B — Explanation
First Waste Driver
Queue–Idle Paradox
Yield Loss Composition
Scheduler Maturity
Block C — Economics & Path
Economic Density Loss
Recoverable Capacity
Inference Persistence Signal
Remediation Path
AI Capacity Operating Pattern
Capacity Waterfall

Architecture Review

The analysis surfaces the yield signal. A structured review maps it to your fleet profile, commitment window, and governance posture — and identifies the sequence of changes that close the yield gap without adding GPUs.

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