Financial AI computing power scheduling
Optimizing AI Computing Power for Financial Industry
Financial AI computing scheduling solution strengthens data management capabilities and AI development environment capabilities based on the GPU computing power pool. The solution not only provides distributed training capabilities and supports model pre-training and model fine-tuning tasks, but also provides one-stop AI computing power platform planning and design services for some financial institutions that lack experience in building AI computing power platforms. Through precise computing resource planning, it ensures that computing power, network, memory and computer room conditions are optimally balanced, thereby eliminating potential performance bottlenecks. In order to meet the financial industry's high computing power demand for intelligent applications in marketing, customer service, investment research and consulting, risk control, transaction analysis and other fields, the Financial AI Computing Power Scheduling Solution relies on efficient computing power scheduling, support for rich AI computing frameworks, and one-stop solutions covering distributed model training, online reasoning, etc., to help financial institutions stand out in the fierce market competition and achieve a leap in intelligent transformation.
Capabilities
Heterogeneous Computing Power Management
The platform enables efficient scheduling of diverse heterogeneous computing resources, including NVIDIA and domestic GPUs. It provides comprehensive management for high-speed IB and RoCE network construction, parallel file storage cluster management, and resource allocation from physical machines to containers.
Detailed Monitoring and Protection
Offers robust monitoring capabilities, including node monitoring, task monitoring, container group monitoring, high-speed network monitoring, and GPU monitoring. This ensures comprehensive oversight, from hardware fault handling to resource usage tracking.
Convenient Model Service
Optimized for efficient model service deployment, the platform supports one-click deployment of online inference services. This significantly enhances the efficiency of model development and deployment workflows.
Rich AI Business Support
Provides extensive AI business support with features like custom image repositories, built-in common computing frameworks, one-click creation of development environments, distributed task management, and automatic mounting of parallel file storage, streamlining common AI processes.
Advanced Resource Management and Scheduling
Delivers dynamic allocation, monitoring, and management of resources, ensuring optimized performance and reduced complexity. The platform supports unified management of heterogeneous computing resources to meet diverse workload demands.
Comprehensive Monitoring and Analytics
Enables in-depth monitoring and analytics of resource consumption, offering detailed insights into GPU usage and network performance. This helps optimize resource allocation and supports proactive system management.
Challenges
Low resource utilization
The inefficient utilization of GPU resources is a significant challenge in AI deployment. Resources are often underused due to the fixed allocation to specific tasks, leading to waste. This problem is especially prominent in dynamic industries where demand fluctuates, causing frequent idle times for GPUs.
Complex calculation process
AI computing tasks, such as data preparation, model pre-training, fine-tuning, and inference, involve complex and lengthy processes. These cycles are often hindered by high technical thresholds and troubleshooting difficulties, making the management of AI workloads a significant challenge.
Performance bottleneck
AI computing centers face performance bottlenecks due to intricate system requirements, including networks, storage, and server configurations. The complexity of the overall infrastructure can lead to slowdowns and inefficiencies, impacting performance during critical tasks like model training and inference.
Difficulty in resource delivery
The rapid delivery of GPU computing resources has become a bottleneck for model pre-training, fine-tuning, and inference.
Advantages
Efficient Computing Power Scheduling
Through a service-oriented approach, our platform enables rapid response and efficient dispatch of computing resources, meeting the high-performance demands of the financial industry. By leveraging GPU resource pooling and intelligent scheduling, we ensure optimal resource utilization, preventing waste and maximizing efficiency across all projects.
Cost Reduction and Full-Stack Integration
We utilize advanced network architecture design and full-stack integration technology to enhance system reliability and availability, reduce complexity, and lower maintenance costs. Additionally, our support for heterogeneous GPUs and optimized resource allocation further streamlines operations, cutting overall costs for our clients.
Improved Business Efficiency with One-Stop Services
Our platform provides a one-stop service experience for the entire AI process, from data preparation and model training to model fine-tuning, reasoning, and services. Combined with features like image warehouses and model squares, we enable rapid delivery of model services, significantly boosting business efficiency and accelerating project timelines.
Flexible and Scalable Expansion
Designed with scalability in mind, our platform supports dynamic expansion and upgrading based on business needs. This ensures that financial institutions can seamlessly scale their GPU resource pools to meet the evolving demands of AI applications and business development over time.