As AI inference workloads become more dynamic, the ability to rapidly distribute large models across heterogeneous cloud resources is a critical infrastructure challenge. Gongji Technology, a startup founded in 2023 by Tsinghua alumni, addresses this by aggregating idle IDC and edge resources into a containerized platform for AI inference, video rendering, and data processing. Their key insight: model distribution must keep pace with elastic compute scheduling. Using JuiceFS, a high-performance distributed file system, they achieve fast model loading and caching across nodes, reducing startup latency and improving resource utilization. This approach is particularly relevant for overseas developers building multi-cloud or hybrid inference pipelines, where model size and network variability are common pain points. The case study offers practical lessons on file system choice, data locality, and scheduling integration for elastic AI infrastructure.
Gongji Technology uses JuiceFS to solve model distribution bottlenecks in cross-cloud elastic inference, aggregating idle resources for AI workloads.