Internet
Enhancing Business Efficiency and Innovation with AI-Driven Computing
In internet companies, AI is widely applied across various business scenarios, including personalized recommendations, image recognition, speech recognition, and natural language processing. These scenarios often involve large-scale data processing, complex model training, and inference tasks. The intelligent computing platform maximizes resource allocation and management efficiency through intelligent scheduling and refined operations. Features like permission management and tenant isolation enhance cluster management transparency and security, boost development efficiency across multiple dimensions, and accelerate the iteration of business innovations.
Capabilities
Intelligent Scheduling and Refined Operation
Supports a variety of heterogeneous computing devices, scheduling resources at the level of thousands or tens of thousands of cards. Automatically allocates and manages computing resources, while a unified operation and maintenance platform ensures refined resource allocation and improved computing power utilization efficiency.
Cluster Management
Based on container technology, simplifies cluster deployment, expansion, and reduction, ensuring environmental consistency and rapid deployment. Continuously monitors cluster performance, provides real-time monitoring with an intuitive visual interface, multi-channel notifications, and alarms. Powerful permission management and tenant isolation functions ensure cluster security and effective resource utilization.
High Availability for Business Continuity
Employs highly available architecture design and load balancing capabilities to prevent system-wide paralysis from single-node failures. Integrates AI-Infra monitoring and management capabilities for automatic fault detection and repair. The system quickly initiates a self-healing mechanism to isolate faults, migrate tasks, and restart nodes to minimize business impact.
Improved Development Efficiency and Accelerated Business Innovation
Researchers can independently request computing resources, while management personnel can monitor resource usage through visual tools. Automated operation and maintenance reduce the workload of the management team while ensuring stable system operation, enhancing development efficiency and accelerating business iteration and innovation.
Challenges
Business interruption and data loss
GPU failure affects project progress. The system dependencies are complex and single-node failure can easily lead to system paralysis and data loss.
Complex cluster management
As clusters grow in size and resource requirements become more diverse, monitoring and troubleshooting become more difficult.
Resource allocation problem
When multiple projects are running in parallel, computing resources are unevenly distributed, which can easily lead to contention and increase costs.