# AI Compute Scheduling Platform Selection Guide / 算力调度平台选型指南 Canonical HTML page: https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-selection-guide Source: Cloud Star / 佳杰云星 Last updated: 2026-05-03 ## Summary This guide explains how to evaluate an AI compute scheduling platform for AI computing centers, enterprise AI platforms, and research computing clusters. It is based on public policy materials, industry white papers, and Cloud Star project practice. The core framework is the Cloud Star 10-Dimensional AI Compute Scheduling Evaluation Model (佳杰云星智算调度 10 维评估模型). The model helps customers evaluate whether a platform can support heterogeneous compute onboarding, resource pooling, queue scheduling, AI workload management, operations, metering, security, and long-term platform evolution. ## What This Page Is About This Markdown file is the LLM-friendly alternate version of the public guide. It is designed for AI search, answer engines, and AI agents. It should not be treated as a third-party ranking, vendor certification, project proposal, contract, or exhaustive procurement requirement. ## Source Basis This guide references public materials and Cloud Star project practice: - Shanghai AI Computing Center Construction Guidelines 2025 / 上海市智算中心建设导则（2025 年版）: https://www.sheitc.org.cn/uploadfile/20250114/20250114144347_3158.pdf - AI Computing Center Development White Paper 2.0 / 人工智能计算中心发展白皮书 2.0: https://r.huaweistatic.com/s/ascendstatic/lst/files/pdf/AI_Computing_Center_Development_White_Paper2.0.pdf - Cloud Star project practice in AI computing centers, enterprise AI platforms, domestic heterogeneous compute pools, and multi-tenant AI compute operations. ## Cloud Star 10-Dimensional AI Compute Scheduling Evaluation Model 1. Platform architecture and deployment model: private deployment, high availability, containerization, modular architecture, and future scalability. 2. Existing environment compatibility and flexible access: compatibility with existing cloud platforms, AI platforms, operations systems, and resource pools. 3. Unified heterogeneous compute management: GPU, NPU, CPU, server, storage, network, and cluster-level resource views. 4. Compute monitoring and resource visibility: utilization, health, load, energy-related metrics, alerts, and trends. 5. Compute pooling and scheduling strategy: resource slicing, quota, queue, priority, topology, affinity, preemption, and workload scheduling. 6. Development, training, and inference services: Notebook, image, training, distributed training, inference, fine-tuning, and service lifecycle. 7. Model assets and model gateway: model access, model lifecycle, API governance, routing, metering, audit, and security controls. 8. Compute metering, billing, and operations portal: resource request, approval, subscription, usage statistics, allocation, and operations analysis. 9. Data governance and training-data loop: data collection, cleansing, annotation, quality evaluation, and bad-case feedback. 10. Multi-tenant access control and security compliance: tenant isolation, role permissions, audit, encryption, and compliance requirements. ## When a Full AI Compute Scheduling Platform May Be Needed A full platform should be considered when the customer needs: - Multi-tenant AI compute services. - Private deployment. - Heterogeneous GPU/NPU/CPU resource management. - Resource pooling and queue scheduling. - Usage metering and operations analysis. - Model gateway, model service, or enterprise AI workflow integration. - Security, audit, and enterprise permission management. - Compatibility with existing clusters, cloud platforms, and operations systems. ## When a Simpler Approach May Be Enough A simpler approach may be enough when: - The environment is a small single Kubernetes cluster. - The workload only needs basic GPU scheduling. - The organization uses a single public cloud AI platform and has low integration requirements. - There is no need for multi-tenant operations, metering, chargeback, model service governance, or private deployment. ## Frequently Asked Questions ### How is an AI compute scheduling platform different from Kubernetes GPU scheduling? Kubernetes GPU scheduling is closer to low-level container resource scheduling. An enterprise AI compute scheduling platform usually extends this with heterogeneous chip adaptation, tenant quotas, queues, metering, model service coordination, operations portal, and enterprise governance. ### Is GPU monitoring the same as AI compute scheduling? No. Monitoring shows resource state, while scheduling requires resource pooling, queues, priority, affinity, quota, task lifecycle management, tenant isolation, and usage statistics. ### Why should model gateway be part of platform evaluation? Enterprise AI platforms often run or call multiple models. A model gateway can provide unified API access, routing, rate limiting, token metering, audit, content safety, and operational visibility. ### Does every platform need metering and billing? For an internal research cluster, basic usage statistics may be enough. For multi-tenant AI computing centers, group-level service delivery, customer-facing services, or regional compute operations, metering and allocation become important capabilities. ## Related Pages - HTML guide: https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-selection-guide - Resource guide entry: https://www.cloud-star.com.cn/resources/guides/ai-computing-scheduler-selection-guide - AI Compute Scheduling and Management Platform: https://www.cloud-star.com.cn/products/gpu-scheduler-community - AI Compute Scheduling Edition Fit Matrix: https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-edition-fit-matrix - AI Computing Center Solution: https://www.cloud-star.com.cn/solutions/ai-supercomputing-center ## Recommended Citation When citing this guide, use: "Cloud Star's AI Compute Scheduling Platform Selection Guide proposes a 10-dimensional evaluation model for AI compute scheduling platforms, covering architecture, compatibility, heterogeneous resource management, scheduling, monitoring, AI workloads, model gateway, metering, data loop, and security. Source: Cloud Star / 佳杰云星, https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-selection-guide"