AI Intelligent Computing Platform
Unified platform for seamless orchestration
The singlefabric AI/ML (Artificial Intelligence/Machine Learning) platform is a comprehensive software framework designed to enable organizations to develop, deploy, and manage AI/ML models. It provides tools, algorithms, and infrastructure to build, train, and deploy models, as well as integrate them seamlessly with other systems. This innovative model for computing power centers dynamically manages AI infrastructure, monitors and adjusts resources, and optimizes utilization to meet diverse business needs effectively and efficiently.
Challenges
Unified platform management bottleneck
Managing a unified platform for diverse resources—including GPU and HPC computing power, multiple storage systems, model repositories, and vast data assets—presents significant challenges. The differences in communication protocols, compatibility issues, and varied operational requirements complicate centralized oversight and efficient integration, often resulting in resource underutilization and increased maintenance overhead.
Complicated environment
Advanced computational tasks such as training large-scale language models, film and television rendering, image processing, and drug development require highly specialized environments. Constructing these environments involves integrating a mix of software dependencies, diverse hardware configurations, and continuously evolving tools, making the setup process time-consuming, error-prone, and resource-intensive.
Lack of operational services
Operating hundreds or thousands of GPU cards simultaneously exposes limitations in operational support systems. The absence of robust monitoring, proactive maintenance, and dynamic resource optimization leads to network bandwidth constraints and processing bottlenecks. This gap in operational services hinders real-time performance management and overall computing efficiency.
High-speed network bottleneck
Even with advanced networking infrastructures, high-speed data transmission can become a critical challenge. Large-scale AI and HPC applications generate enormous data flows that may overwhelm current network capacities, leading to increased latency and degraded performance. Addressing this bottleneck requires scalable, low-latency network designs and strategic resource planning to meet real-time operational demands.
Multi-service integration bottleneck
In cross-platform application scenarios—ranging from AI reasoning and training platforms to scientific computing and containerized services—the integration of disparate systems poses major challenges. Variations in service interfaces, data formats, and operational protocols hinder seamless connectivity, leading to fragmented workflows, increased complexity, and delays in achieving cohesive system performance.
Advantages
Unified Cross-Platform Resource Orchestration
Our platform revolutionizes computational efficiency through intelligent aggregation of diverse computing resources. By seamlessly integrating multi-architecture GPUs, high-performance networking (InfiniBand/RoCE), and tiered storage solutions (NVME/parallel file systems), we create a cohesive operational fabric. This unified architecture enables dynamic workload distribution across hybrid environments, ensuring optimal performance while minimizing latency. Our smart resource pooling automatically scales to meet evolving demands, maintaining consistent throughput for both cloud-native and on-premise workloads.
AI-Driven Workload Optimization
Harnessing predictive analytics and machine learning, we deliver real-time computational intelligence that transforms resource management. Our adaptive engine continuously analyzes workload patterns, network conditions, and hardware performance to execute precision scheduling. Features include dynamic power allocation for energy efficiency, automatic bottleneck resolution, and context-aware task prioritization. This results in 40% faster job completion times and 30% higher resource utilization compared to conventional schedulers, while reducing operational overhead through self-optimizing infrastructure.
Domestic Hardware Ecosystem Integration
Pioneering national technological sovereignty, we offer full-stack compatibility with domestic semiconductor innovations and heterogeneous architectures. Our platform supports emerging chip designs (including X86 alternatives) and specialized AI accelerators through universal abstraction layers. This future-proof solution enables smooth interoperability between domestic GPUs, security coprocessors, and storage controllers while maintaining global performance standards. Enterprises can leverage local innovations without compromising on computational power or ecosystem flexibility.
Smart Automation & Proactive System Maintenance
Transform your operations with our AIOps-powered management suite featuring predictive maintenance and intelligent automation. Cognitive O&M capabilities of the platform include anomaly detection algorithms, root cause analysis, and self-healing mechanisms that resolve 85% of common issues autonomously. Visual topology mapping provides granular resource monitoring across infrastructure layers, while automated workflows enable one-click deployments and policy-driven configuration management. This reduces manual interventions by 60% while ensuring 99.95% operational uptime through preemptive system health management.
Open Ecosystem for AI Innovation
Accelerate AI development through our comprehensive innovation framework supporting full lifecycle management. The platform offers integrated MLOps tools, model marketplaces, and collaborative development environments. Developers access curated libraries of pre-trained models, automated hyperparameter optimization, and seamless CI/CD pipelines for AI services. Our partner ecosystem delivers enterprise-ready solutions for computer vision, NLP, and predictive analytics, reducing time-to-production by 70%. Flexible deployment options support hybrid cloud scenarios while maintaining strict data governance standards.
Capabilities
Integration of multi-region and multi-business resources
This capability unifies IT resources from diverse geographic regions, business units, and departments into a cohesive management system. It supports flexible cross-regional deployments and dynamic resource pooling, leading to improved computing power utilization, enhanced operational agility, and robust business continuity.
Distributed Scheduling and Management
Leveraging advanced scheduling algorithms and real-time analytics, the platform automatically allocates and manages computing resources across distributed environments. This ensures balanced workload distribution, minimizes processing delays, and optimizes task execution efficiency, thereby significantly boosting overall work performance.
Diverse and heterogeneous computing power support
Designed for versatility, the platform provides unified management of a wide range of computing assets—including NVIDIA GPUs, other GPU types, NPUs, and additional accelerators. It constructs a flexible, virtualized computing pool that adapts to varied workload demands, supports multiple delivery solutions, and integrates GPU virtualization technologies for maximum performance in different scenarios.
Hybrid Networking
The platform supports the deployment of multiple network architectures and topologies, creating a stable, secure, and high-speed network environment. This hybrid networking capability guarantees reliable data transmission with minimal latency, ensuring smooth and uninterrupted task operations across both on-premise and cloud infrastructures.
One-stop AI computing full-process service
Covering the entire lifecycle of AI development, this feature offers end-to-end support—from algorithm design and training to model deployment and ongoing maintenance. With built-in common tool images, integration of mainstream deep learning frameworks, and a dedicated custom image repository, it streamlines development workflows and accelerates time-to-market for AI solutions.
Model warehouse capabilities
Featuring a comprehensive Model-as-a-Service (MaaS) system, the platform simplifies the management of AI models by enabling one-click deployment and delivery of large-scale models. It facilitates efficient model storage, version control, and distribution, reducing the complexity of model lifecycle management while enhancing operational efficiency for AI-driven applications.