Business Challenge
-
Network connectivity stability
Network connections in edge environments must be stable and reliable to ensure continuous data transmission and accurate inference results.
-
Real-time decision-making demands
Application scenarios such as autonomous driving and quantitative trading require real-time data analysis and decision-making, where any delay could lead to serious consequences.
-
Large language model inference latency
Large language model inference typically requires significant computing resources, resulting in unacceptable latency when processed in traditional centralized data centers.
-
Data security and privacy
The data processed in edge inference may contain sensitive information, necessitating the assurance of data security and privacy protection.
-
System scalability and maintenance
As business demands grow, edge computing systems need to be easily scalable and maintainable to accommodate evolving workloads.
-
Resource limitations
Edge devices typically have limited resources, such as computing power, storage space, and energy supply, which restricts their capability to handle complex tasks or large sets of data.
Solutions
High-density cabinets: Deploy high-density cabinets at the edge to provide powerful local computing capabilities, supporting the low-latency requirements of large-scale model inference.
Bandwidth, operator dedicated lines, and low-latency network: Ensure high-speed and stable connections between edge nodes and the core network to reduce data transmission latency and enhance customer experience.
IP address leasing/sale, and cloud connectivity/DCI: Allocate unique IP addresses to edge nodes and seamlessly integrate with cloud resources through cloud connectivity services.
VPN/SD-WAN: Ensure the security and efficiency of data transmission through virtual private networks (VPN) and software-defined wide-area networks (SD-WAN).
Customized computing power and data center agent-construction and transformation: Customize the construction or transformation of edge data centers according to specific application requirements to optimize the performance of large-scale model inference.
Computing power and data center operations and maintenance: Provide professional operations and maintenance services to ensure the stable operation and performance optimization of edge computing environments.
Computing power network services: Offer network architecture design and optimization services for edge computing to support efficient data flow and processing.
Computing power equipment leasing/sale, and computing power leasing: Offer flexible options for purchasing or leasing computing power equipment according to business needs to rapidly expand edge computing capabilities.
IT equipment operations and maintenance: Deliver professional operations and maintenance support for IT equipment in edge environments to ensure system stability and reliability.
