The model inference solution provides customers with comprehensive, flexible and efficient inference services, covering application scenarios such as AI computing cloud, AI intelligent computing platform, integrated hardware and software systems and edge nodes.
Under traditional models, enterprises often face challenges such as limited computing resources, high maintenance costs, and slow response times, which hinder the rapid development of AI projects.
The rapid development of AI technology makes it difficult for non-professional developers to get started quickly, while model deployment and optimization have become significant technical bottlenecks.
Business requirements are constantly evolving, yet traditional IT architectures struggle to respond flexibly or scale on demand.
For application involving sensitive data, how to perform efficient reasoning while ensuring efficient inference while maintaining data security and privacy protection remains a major challenge.
This solution integrates world-leading AI technologies and extensive computing resources to help users easily manage the entire process from model access to efficient deployment, accelerate the implementation of AI applications, and enhance business intelligence.
It integrates leading industry models including ChatGLM, Baichuan, LLaMA and supports other large models, supports one-click deployment to meet diverse AI application needs. Users can also upload custom models to an image repository and leverage online inference services for rapid deployment, enabling seamless integration of AI capabilities into business processes.
The platform provides diverse computing including NVIDIA GPUs, Ascend GPUs, Hygon DCUs and CPUs, to accommodate different business scenarios. Supports flexible resources scheduling and dynamically adjusts allocations based on workload, improving overall resource utilization.
Model applications are encapsulated using containerization technology to ensure a consistent deployment environment and simplified operations, thereby reducing operational and maintenance complexity. This approach enhances system security and isolation, safeguards user data and prevents data cross-contamination. Compatible with mainstream AI frameworks such as PyTorch, TensorFlow, PaddlePaddle, and supports multiple technology stacks to accommodate different developer preferences.
The platform provides efficient cluster management capabilities, enabling inference cluster to be created within minutes. It supports elastic scaling based on workload demands to ensure high service availability and fast response times.