AI Deployment Made Easy: How KubeSphere LuBan Accelerates DeepSeek-R1 Scaling

Eliminating Deployment Complexity for Large AI Models

Large AI models like DeepSeek-R1 offer impressive capabilities, but deploying them in real-world applications often comes with major hurdles: complex deployment workflows, resource management challenges, and high operational costs. These obstacles can slow down innovation and make AI adoption difficult.

To address this, the KubeSphere community has developed a DeepSeek-R1 extension based on the LuBan architecture — in just 3 days. The result? A streamlined, 3-minute deployment process using a visualized interface, making AI deployment accessible to all developers.

Why KubeSphere? Three Key Advantages

1. Simplified & Standardized Deployment

By integrating Ollama runtime and NextChat UI, KubeSphere provides a complete end-to-end solution for AI model deployment. Developers can easily load models, manage services, and monitor performance — all from the KubeSphere control panel, with the same ease as managing microservices.

2. Scalable & Flexible AI Infrastructure

KubeSphere is designed for scalability, supporting models of varying sizes — from 1.5B to 671B parameters — with a modular and flexible approach. Whether you’re working on a small prototype or deploying a full-scale AI service, KubeSphere adapts to different workloads effortlessly.

3. Intelligent Resource Management & Cost Optimization

With multi-tenant management and dynamic resource scheduling, KubeSphere enables enterprises to allocate GPU resources efficiently. This prevents resource wastage and ensures that compute power is optimally distributed across workloads.

The Power of KubeSphere’s LuBan Architecture

KubeSphere leverages the LuBan architecture to provide flexible, scalable, and efficient AI deployment on Kubernetes. Its key capabilities include:

• Modular Design: Functions are split into independent microservices, allowing for seamless customization and integration.
• High Scalability: Each module can operate independently and communicates via APIs or message queues, ensuring flexible expansion.
• High Availability & Fault Tolerance: Built on Kubernetes HA features, LuBan automatically recovers from failures, ensuring uninterrupted operation.
• Multi-Tenant Support: Provides secure isolation for different AI workloads, making it ideal for cloud-native deployments.

Building the Solution in Just 3 Days

• Day 1: Developed the extension framework using LuBan
• Day 2: Integrated DeepSeek-R1 with Ollama runtime for containerized deployment
• Day 3: Integrated the NextChat UI and optimized service orchestration

Thanks to KubeSphere’s modular architecture, our team could focus 80% of efforts on AI logic, rather than dealing with infrastructure complexities. This LEGO-like development approach accelerated the entire process.

Technology Stack

• Ollama: An AI execution platform that enables fast loading and inference for large models.
• NextChat: A chatbot framework that combines NLP and ML for seamless conversational AI experiences.

How It Works: The Deployment Workflow

1. Start Ollama Server: Launch ollama serve to provide an OpenAI-compatible API.
2. Send a Request: Use ollama client to trigger model execution.
3. Model Loading: If the required model is not available in /root/.ollama/models, it is automatically pulled from a repository.
4. Interactive UI: Users interact with DeepSeek-R1 through NextChat, enabling multi-turn AI conversations.

Installation & Usage Guide

• Install NVIDIA GPU Operator (Optional)

• Deploy DeepSeek

• Access DeepSeek Chat

🔗 Code Repository: KubeSphere Extensions
📄 DeepSeek Extension Guide: DeepSeek-R1 Extension README (Detailed setup and usage guide)
📖 Developer Guide: Extension Development Guide
🚀 Installation Instructions: KubeSphere Quick Install

Final Thoughts: AI Deployment, Simplified

This project demonstrates how KubeSphere’s LuBan architecture makes AI model deployment simpler, more efficient, and highly scalable. By integrating DeepSeek-R1 into KubeSphere, we provide developers with a powerful, yet easy-to-use AI deployment solution.

With flexible deployment options, optimized resource management, and seamless integration, this initiative showcases the best of AI and cloud-native technology.

We hope this inspires more developers to explore Kubernetes-based AI deployments. Let’s push the boundaries of AI & cloud-native computing together!

Join the Conversation

🔗 Follow KubeSphere on GitHub: KubeSphere GitHub
💬 Discuss on LinkedIn & Twitter/X: Use #KubeSphere #AI #Kubernetes

About KubeSphere

KubeSphere is an open source container platform built on top Kubernetes with applications at its core. It provides full-stack IT automated operation and streamlined DevOps workflows.

KubeSphere has been adopted by thousands of enterprises across the globe, such as Aqara, Sina, Benlai, China Taiping, Huaxia Bank, Sinopharm, WeBank, Geko Cloud, VNG Corporation and Radore. KubeSphere offers wizard interfaces and various enterprise-grade features for operation and maintenance, including Kubernetes resource management, DevOps (CI/CD), application lifecycle management, service mesh, multi-tenant management, monitoring, logging, alerting, notification, storage and network management, and GPU support. With KubeSphere, enterprises are able to quickly establish a strong and feature-rich container platform.