AI-Stack: Key Solutions for Unleashing AI Infrastructure

In an era where AI and deep learning have become core competitive advantages for enterprises, the performance of AI software relies on stable and efficient computing resource support. Traditional server architectures and deployment models can no longer meet the massive computational power and flexible resource scheduling demands required by today’s AI model training and inference. The AI-Stack platform built by INFINITIX addresses this challenge by providing a comprehensive, modular, and scalable AI infrastructure management and GPU resource scheduling solution.

1. Modern Challenges in AI Software and Server Integration

As AI model scales continue to expand from millions to hundreds of billions of parameters, the demands for computational power, memory, and I/O performance during AI project training and inference phases have become increasingly stringent. When building and scaling AI infrastructure, enterprises face not only the complexity of technology selection but also need to balance operational costs, scalability, and usage efficiency. Particularly in an environment of rising GPU prices and hardware diversification, how to effectively integrate heterogeneous resources, avoid hardware idle time, and support multi-tenant sharing has become one of the biggest obstacles to enterprise AI strategy implementation.

AI models’ computational resource requirements are growing exponentially, and enterprises commonly face:

2. AI-Stack’s Four Core Solutions

AI-Stack serves as the central hub for enterprise AI infrastructure management. Its role is not just as a single resource scheduler, but as an engine that integrates global resources, coordinates computing tasks, and achieves automated and intelligent operations. By combining bare-metal GPU virtualization, Kubernetes native integration, dynamic scaling, and visual control management, AI-Stack provides a consistent operational experience for data scientists and IT administrators while ensuring the efficiency and stability of AI computing tasks. Whether in single deployment environments or multi-node distributed architectures, AI-Stack ensures optimal allocation of computational resources and reliable task execution, becoming the key to enterprise infrastructure strategy for autonomy, sustainability, and efficiency in the AI era.

1. One-Stop AI Software and Hardware Integration

• Integrated management and monitoring of GPU resources from two major manufacturers
• Integration with mainstream AI frameworks: TensorFlow, PyTorch, JAX
• Built-in development tools: Jupyter Notebook, VS Code Remote
• Visual dashboard: monitoring GPU, CPU, RAM, temperature, and power consumption

2. Flexible Deployment and Cloud-Ground Integration

• Hybrid cloud deployment: supporting hybrid architecture combining on-premises and public cloud
• GPU as a Service (GaaS): pay-as-you-use, reducing capital expenditure
• Private cloud construction support: customized hardware procurement and maintenance services
• Real-time updates for latest GPU models

3. Containerization + MLOps Automation Process

• Docker + Kubernetes architecture: environment consistency, version control
• Automated CI/CD workflows: one-click deployment, real-time inference service launch
• Scheduling algorithms with flexible multi-strategy scheduling to comprehensively improve computational efficiency
• Distributed training support: Horovod, DeepSpeed, Slurm

4. Intelligent Resource Management and Multi-Tenant Support

• GPU virtual partitioning (ixGPU): supporting NVIDIA/AMD, achieving single-card multitasking
• GPU aggregation technology: multi-card collaboration to enhance large model training performance
• Multi-tenant permission and isolation mechanisms: RBAC and resource quota control

3. AI-Stack’s Advantages Compared to Traditional Platforms

AI-Stack’s advantages are not only reflected in technical functionality but, more importantly, it completely redefines the operational model of AI infrastructure. Traditional platforms often adopt static, closed architectures that struggle to respond to the uncertainty and diversity of AI workloads. AI-Stack, with cloud-native design as its core, combined with bare-metal GPU virtualization and multi-node dynamic resource management capabilities, can respond in real-time to different model training, inference, and testing requirements. Its support for cross-GPU brand scheduling and monitoring, multi-tenant flexible isolation, and ESG carbon emission control makes it the best platform choice for enterprises to implement AI strategies and sustainable governance.

4. Application Results and Industry Cases

INFINITIX AI-Stack connects the entire process management from “AI developers” to “IT administrators.” Through a highly integrated software and hardware platform, it helps enterprises build efficient, flexible, secure, and scalable AI computing environments, serving as the best support for enterprises’ digital transformation journey toward the AI era.