LiblibAI needed stable high-performance infrastructure for large-scale model training, low-cost inference, overseas access, and storage lifecycle management so its AI-creation platform could keep up with user growth and content-generation workloads.
Reported impact
LiblibAI improved compute efficiency and platform availability, optimized cross-border experience for users, and kept storage and serving costs under better control as the creation platform scaled.
LiblibAI built its AI-creation platform on Baidu AI Cloud using BaiGe heterogeneous compute, Qianfan model tooling, CDN services, and supporting storage architecture. The compute layer is designed for large-scale deep-learning workloads, giving model and operations engineers full lifecycle management, train-infer acceleration, fault tolerance, and intelligent diagnostics. For online text-to-image and related creation services, Baidu provides the large GPU footprint, stable high-throughput networking, and RDMA-compatible training environment required by LiblibAI's platform. The solution also addresses global user distribution: CDN and cross-border acceleration help API requests and user uploads move reliably between overseas users and domestic storage. Meanwhile, tiered storage and archival support keep training datasets and inference workloads economical.
Model-training jobs and inference requests are scheduled on the BaiGe platform, while user uploads and API traffic are accelerated through CDN and cross-border network paths. Tiered storage holds datasets and archives, and the resulting generated-content services are served back to users through the same elastic infrastructure.
BaiGe gives model and operations experts cluster lifecycle support, train-infer acceleration, fault tolerance, and intelligent diagnostics, with effective training time above 98% (original: 百度百舸异构计算平台…提供全面的集群运维支持和任务全生命周期管理，同时具备训练/推理加速、故障容错、智能故障诊断等高级功能，有效训练时长可达98%以上).
Baidu also provided global acceleration and cross-border upload acceleration for users in the US West Coast and Southeast Asia (original: 最终用户覆盖在美西和东南亚国家，为客户提供全球加速服务…并且提供跨国回源加速的上传服务能力).
LiblibAI built a one-stop AI creation platform on Baidu's heterogeneous compute platform to support stable training and inference for content creation (original: LiblibAI基于百度智能云异构计算平台构建高性能AI创作系统，实现模型训练与推理业务的高效稳定运行).
LiblibAI needed large GPU capacity, stable high-throughput networking, and a mature RDMA multi-node multi-GPU training platform with tiered and archival storage (original: 提供大规模GPU显卡…模型训练需要有稳定的、高吞吐的网络性能…支持英伟达多机多卡RDMA架构的训练平台，并且提供高性能存储和归档存储).
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