AI Intelligent Computing Machine
Accelerating AI Development and Deployment
The integrated hardware and software AI development platform is designed to accelerate the development and deployment of AI applications. The high-performance NVIDIA GPU and AMD, Intel GPU + rich AI software stack are closely combined to provide an out-of-the-box AI development environment to help users quickly build, train and deploy AI models.
Advantages
Integrated AI Stack
Pre-installed software stack with optimized hardware configuration for immediate AI development and training.
Intelligent Resource Management
Advanced scheduling system with pre-trained models and container images for rapid deployment.
Collaborative Development
Multi-user environment with mainstream tools and segmented computing resources for team efficiency.
Accelerated Processing
High-performance GPU clusters with optimized networking for faster model training and inference.
Capabilities
Resource Management
Flexible configuration of computing resources allows for dynamic scaling and policy-based management, enabling the efficient allocation of CPU, memory, and GPU resources to match workload demands. Efficient storage systems, such as distributed architectures with data deduplication and compression, enhance performance while saving space, and multi-tenant isolation ensures security and resource independence through namespace isolation, virtual private clouds (VPCs), and role-based access control (RBAC). For virtual GPUs (vGPUs), granular allocation and dynamic reconfiguration support tailored performance for diverse workloads, from AI training to graphical rendering, with compatibility across various hypervisors and APIs like NVIDIA CUDA. These features together optimize resource utilization, scalability, and security in modern computing environments.
AI Development
Elastic Container Instances (ECIs) provide on-demand, serverless container solutions, enabling developers to run containerized applications without managing underlying infrastructure. This allows for seamless scaling and cost efficiency as containers are provisioned only when needed. Distributed training acceleration leverages parallel computing across multiple nodes or GPUs to significantly enhance the performance of large-scale machine learning tasks, reducing training time for complex models. Integration with mainstream development tools, such as IDEs (e.g., Visual Studio Code), CI/CD pipelines (e.g., Jenkins, GitLab CI), and container orchestration platforms (e.g., Kubernetes), streamlines the development workflow, enabling developers to build, test, and deploy applications more efficiently while maintaining compatibility with modern software ecosystems.
Model Registry
A rich preset model library offers a diverse collection of pre-trained models across various domains, such as natural language processing, computer vision, and recommendation systems, providing developers with ready-to-use solutions for common tasks. One-click model deployment simplifies the process of deploying these models to production environments, allowing users to quickly integrate AI capabilities without extensive setup or configuration. Additionally, support for custom models ensures flexibility, enabling developers to upload, train, and deploy their unique models tailored to specific business needs, fostering innovation and adaptability in diverse application scenarios. Together, these features enhance the accessibility and efficiency of AI development and deployment workflows.
Mirror Repository
Built-in common tool images provide pre-configured, ready-to-use environments for popular tools and frameworks, streamlining development and deployment processes by reducing setup time. Support for custom images allows users to create and manage containerized environments tailored to specific requirements, offering flexibility for unique workloads and applications. Image version management ensures efficient tracking, updating, and rollback of container images, enabling seamless transitions between versions and maintaining system stability. Together, these features enhance the efficiency, customization, and reliability of containerized application workflows.