Financial GPU pooling
Efficient GPU Resource Pool Management for AI Applications in the Financial Industry
The GPU computing power pooling solution forms a GPU resource pool by centrally managing multiple homogeneous or heterogeneous GPU servers. This resource pool realizes unified management and dynamic allocation of GPU resources through the resource management and scheduling system. By integrating multiple GPU servers, we build an efficient shared GPU resource pool, and through the resource management and scheduling system, we achieve unified management and dynamic allocation of GPU resources. This not only reduces the operation and maintenance costs and risks of financial institutions, but also greatly improves the utilization rate of GPU resources, flexibly supports the GPU computing needs of various scenarios in the financial industry, and easily copes with the challenges of financial institutions for AI applications such as data processing, RAG optimization and model reasoning.
Capabilities
Heterogeneous GPU Support
Compatible with various mainstream GPU products, including NVIDIA, AMD, Intel. This ensures flexibility and supports diverse GPU needs across different platforms and environments.
GPU Resource Pooling and Virtualization
Enables the central deployment of multiple GPU servers to form a unified GPU resource pool. Utilizing virtualization technology, physical GPUs are converted into multiple vGPUs, allowing for flexible allocation of resources across various tasks, including dedicated and shared resource pools.
Advanced Resource Management and Scheduling
Optimizes GPU resource utilization through an advanced management and scheduling system, offering dynamic allocation, monitoring, pooling, and segmentation. This reduces complexity and improves resource efficiency, enhancing the overall system performance.
Mirror Repository for Deep Learning
Provides a repository of commonly used deep learning images for quick setup of operating environments. Users can build and manage custom images, leveraging basic images or Dockerfiles for faster development and model training.
Model Deployment and Inference Services
Supports quick deployment of AI models through Model Square, offering open-source models like LLaMA, ChatGLM, and Baichuan. It enables seamless model inference services and the option to deploy third-party or self-built models for external inference services.
Comprehensive Resource Consumption and Analytics
Tracks GPU resource consumption in real-time, providing detailed statistics and reporting tools to offer visibility into usage patterns, helping optimize cost management and billing for both resource allocation and actual usage.
Challenges
Low resource utilization
In traditional GPU deployment methods, GPUs are directly bound to specific servers or applications, resulting in low GPU resource utilization. Especially in the financial industry with high business diversity and volatility, GPU resources are often idle and wasted.
Complex management
The financial industry has a large number of servers and GPU resources. Traditional management methods require individual configuration and maintenance of each server and GPU, resulting in low management efficiency and high maintenance costs.
High Cost
Purchasing, deploying, and maintaining large quantities of GPU resources requires high costs, which is a considerable burden for financial institutions.
Advantages
Efficient resource utilization
We leverage GPU resource pooling and intelligent scheduling to ensure optimal resource usage, preventing resource waste and maximizing efficiency in every aspect of the project.
Cost reduction
By supporting heterogeneous GPUs and optimizing resource allocation, management, and maintenance, we streamline operations, reduce complexity, and lower overall costs for our clients.
Improved business efficiency
With advanced features like image warehouses and model squares, we enable rapid delivery of model services, enhancing the efficiency of business processes and accelerating project timelines.
Flexible expansion
Our platform allows for the dynamic scaling of the GPU resource pool according to changing business needs, ensuring that AI applications are supported even as demand fluctuates over time.