This section captures essential project identification and cloud environment details to ensure proper tracking, cost allocation, and governance compliance for the exceptional resource request.
Project Name
Project ID or Code
Cost Center Allocation Code
Primary Cloud Service Provider
Amazon Web Services (AWS)
Google Cloud Platform (GCP)
Microsoft Azure
Oracle Cloud Infrastructure (OCI)
Other
Specify Other Cloud Provider
Target Regions and Availability Zones
Project Owner Full Name
Project Owner Email
Technical Lead Full Name
Technical Lead Email
Business Justification Category
Strategic AI Initiative
Customer Delivery Commitment
Research Breakthrough Opportunity
Competitive Emergency
Regulatory Compliance
Performance Benchmarking
Other
Describe Other Business Justification
Priority Level
P0 - Critical Business Impact
P1 - High Business Impact
P2 - Medium Business Impact
P3 - Low Business Impact
Related Project IDs or Resource Tags
Compliance Requirements Applicable
ISO 27001
SOC 2 Type II
GDPR Data Processing
HIPAA
PCI DSS
FedRAMP
Internal Data Governance
No Special Compliance
Provide detailed technical specifications for the compute cluster architecture, including hardware accelerators, node configuration, and estimated runtime. This information is critical for capacity planning and cost modeling.
Primary Compute Accelerator Type
NVIDIA GPUs
AMD GPUs
Google TPUs
AWS Trainium/Inferentia
CPU-Only Cluster
Mixed Accelerators
Specific NVIDIA GPU Model
H100 SXM5
H100 PCIe
A100 80GB
A100 40GB
V100
RTX 6000 Ada
Other
Specify NVIDIA GPU Model
Specific AMD GPU Model
MI300X
MI250X
MI210
Other
Specify AMD GPU Model
Specific Google TPU Version
TPU v5p
TPU v5e
TPU v4
TPU v3
Other
Specify TPU Version
Total Number of Accelerator Devices Required
Accelerators per Node
Total Number of Compute Nodes
CPU Cores per Node
System RAM per Node (GB)
Inter-node Network Fabric
InfiniBand NDR (400 Gbps)
InfiniBand EDR (100 Gbps)
InfiniBand FDR (56 Gbps)
Ethernet 400 Gbps
Ethernet 100 Gbps
Standard VPC Networking
Not Applicable
Storage Architecture Details
Estimated Network Bandwidth to Internet (Gbps)
Requested Cluster Start Date/Time (UTC)
Estimated Duration (Hours)
Maximum Tolerable Duration Extension (Hours)
Primary Workload Type
Large Language Model Training
Computer Vision Model Training
Scientific Simulation
Deep Reinforcement Learning
Model Fine-tuning
Distributed Inference
Multi-modal Training
Other
Describe Workload Type
Machine Learning Framework and Version
Will this utilize distributed training across all nodes?
Describe distributed training strategy (e.g., data parallel, model parallel, pipeline parallel, ZeRO optimization stages)
Explain why distributed training is not used and how nodes will be utilized
Checkpointing Strategy and Frequency
Expected Model Flops Utilization (MFU) Percentage
Complete the detailed cost analysis below. The table will automatically calculate totals based on your inputs. This section must demonstrate thorough financial planning and justify the exception request.
Detailed Cost-per-Hour Breakdown
Resource Category | Specific Component | Unit Quantity | Cost per Unit per Hour | Subtotal per Hour | Pricing Model (On-Demand/Reserved/Spot) | ||
|---|---|---|---|---|---|---|---|
A | B | C | D | E | F | ||
1 | Compute Accelerators | NVIDIA H100 SXM5 GPUs | 64 | $8.50 | $544.00 | On-Demand | |
2 | Compute Instances | High-Memory CPU Nodes | 8 | $12.00 | $96.00 | On-Demand | |
3 | Interconnect | InfiniBand NDR Fabric | 8 | $3.50 | $28.00 | On-Demand | |
4 | Storage | High-IOPS NVMe Local SSD | 8 | $2.25 | $18.00 | On-Demand | |
5 | Network | Data Transfer Egress | 1 | $0.80 | $0.80 | Pay-as-you-go | |
6 | $0.00 | ||||||
7 | $0.00 | ||||||
8 | $0.00 | ||||||
9 | $0.00 | ||||||
10 | $0.00 |
Total Estimated Cost per Hour
Total Estimated Cost for Full Duration
Is this cost already budgeted in current fiscal cycle?
Budget Line Item Reference
Explain Unbudgeted Financial Impact and Funding Source
Alternative Approach Cost (if cluster not provisioned)
Cost Optimization Measures Already Applied
Have you evaluated using preemptible/spot instances for cost reduction?
Explain why spot instances are not suitable for this workload
Return on Investment (ROI) Classification
Direct Revenue Generation
Strategic Capability Development
Customer Retention Critical
Technical Debt Reduction
Competitive Positioning
Regulatory Requirement
Other
Detailed ROI Justification
Financial Risk Level of This Expenditure
Define clear operational guardrails to prevent cost overruns from idle resources. This section establishes automated controls and manual intervention protocols for responsible resource lifecycle management.
Will auto-scaling be implemented to handle dynamic workload demands?
Describe Auto-Scaling Policy
Justify why fixed cluster size is required and how you will prevent resource waste
Idle Detection Threshold (GPU Utilization %)
Maximum Allowed Idle Time Before Alert (Minutes)
Maximum Allowed Idle Time Before Automatic Decommissioning (Minutes)
Decommissioning Triggers (Select all that apply)
Training job completed successfully
Training job failed with unrecoverable error
Idle timeout threshold exceeded
Manual intervention by technical lead
Cost budget threshold reached
Maximum duration exceeded
Checkpoint saved and verified
Notification Channels for Idle Alerts
Will data be automatically backed up before decommissioning?
Specify Backup Destination and Retention Policy
Explain why data backup is not required and how data loss risk is mitigated
Resource Tagging Strategy for Cost Tracking
Will you implement real-time cost monitoring dashboards?
Dashboard URL or Monitoring Tool
Emergency Contact Information for 24/7 Cluster Issues
Have you tested the decommissioning procedure in a staging environment?
Explain plan to validate decommissioning before production deployment
Final approval section requiring technical and executive authorization. All signatories must review complete form before attesting to accuracy and business justification.
Lead Solutions Architect Full Name
Lead Solutions Architect Email
I certify that the architectural specifications are optimal and no cost-effective alternatives exist
I have reviewed the auto-scaling and decommissioning protocols and confirm they are operationally sound
I confirm that idle resource monitoring will be actively managed by the technical team
Solutions Architect Technical Risk Assessment
Solutions Architect Confidence Score (1-5)
VP of Engineering Full Name
VP of Engineering Email
I approve the unbudgeted expenditure based on presented business justification
I acknowledge the financial risk and accept responsibility for cost overruns if idle resource protocols fail
I confirm this request aligns with organizational strategic priorities
VP of Engineering Business Justification Endorsement
Does this require additional C-level executive approval?
Additional Approver Name and Title
Lead Solutions Architect Digital Signature
VP of Engineering Digital Signature
Final Approval Timestamp
To configure an element, select it on the form.