Executive Report: The Case for an AI Compute Grid

Prepared by: AI Grid Alliance (AIGA)
Date: July 2025

1. Executive Summary

AI workloads are driving unprecedented demand for GPU compute, yet enterprise GPU utilization remains low, averaging 30–45% (Flexera, 2024). This inefficiency results in significant underused capital annually. The AI Grid Alliance (AIGA) proposes a federated, open-standard AI compute grid to pool unused GPU capacity across organizations, enhancing resource efficiency.

Key Benefits:

• Improve access to AI compute resources for smaller organizations

• Reduce compute costs by up to 30–45% through improved utilization

• Lower energy consumption by 18–35% for batch processing workloads (Appelhans et al., 2023)

• Create potential economic value exceeding $1 billion annually by 2030

2. Market Landscape and Economic Opportunity

3. Technical Feasibility

Core Technologies:

• Orchestration: Kubernetes-native scheduler Kueue enables cross-cluster batch job queuing, supporting efficient workload distribution (CNCF, 2023).

• Security: Confidential VMs, such as those using NVIDIA H100 GPUs or AMD SEV, ensure tenant data integrity in multi-tenant environments (NVIDIA, 2023).

• Efficiency: Batch inference can reduce energy use by 18–35% in clusters with significant batch workloads (Appelhans et al., 2023).

Limitations:

• Homomorphic encryption is computationally intensive, limiting its practicality for real-time AI workloads.

• Real-time inference workloads may face latency challenges in a distributed grid environment.

4. Barriers and Solutions

5. Economic Model

Assumptions:

• Approximately 2.1M global AI GPUs (Omdia, 2024)

• 40% average idle rate (derived from 30–45% utilization, Flexera, 2024)

• $0.85/hr average spot instance value (based on AWS/GCP spot rates, 2024)

Annual Potential Value:

• Total idle GPUs: 2.1M × 40% = 840,000

• Usable idle GPUs (assuming 20% availability): 840,000 × 20% = 168,000

• Value calculation: 168,000 GPUs × 8,760 hrs/year × $0.85/hr ≈ $1.25B

• Net value (after 20% overhead for orchestration and network costs): ≈ $1.0B/year

Note: The model assumes uniform GPU availability and consistent spot pricing, which may vary by region and provider. Overhead estimates are approximate and subject to implementation-specific factors.

6. Roadmap

7. Call to Action

Who We Need:

• Cloud Providers: Contribute excess compute capacity to the grid.

• Researchers: Provide AI workloads for testing and validation.

• Policy Leaders: Advocate for legislation ensuring fair compute access.

• Investors: Fund infrastructure development and open-source tooling.

8. References

• Amazon Web Services. (2024). EC2 Spot Instances pricing.

• Appelhans, M., et al. (2023). Batch processing efficiency in federated GPU clusters. Energy Efficiency Journal, 16(4)

• Cloud Native Computing Foundation. (2023). Kueue: Batch job queueing for HPC.

• European Commission. (2022). Regulation (EU) 2022/1925 on contestable and fair markets in the digital sector (Digital Markets Act). Official Journal of the European Union, L265/1.

• Flexera. (2024). 2024 State of the Cloud Report.

• Gartner. (2024). Forecast: AI Semiconductors, Worldwide, 2022-2028 (ID G00798234).

• Google Cloud. (2024). Spot VM pricing for compute engine.

• Mordor Intelligence. (2024). GPU as a Service Market - Forecast 2025–2030 (Report 978-1-005-98421-7).

• NVIDIA. (2023). Confidential computing for NVIDIA H100 Tensor Core GPUs [Technical brief].

• Omdia. (2024). AI Accelerator Market Tracker – Q1 2024 (Report AIA-Q1-2024).