Remote-Access GPU-Isolation Docker Architecture for LLMs Enhanced Programming Education

Large Language Models (LLMs) have revolutionized programming education and Continuous Integration/Continuous Deployment (CI/CD) workflows by providing context-aware tutoring and conversational diagnostics. However, scaling these tools in multi-user GPU environments presents several challenges, including remote access complexity and GPU container contention. This paper proposes a Docker-based framework that uses the NVIDIA Container Toolkit to create isolated GPU workspaces for each student. By integrating Apache and NGINX reverse proxies, we unify SSH and IDE traffic over HTTP(S) and Web-Socket protocols, centralizing TLS termination and streamlining firewall management. Our system supports horizontal scaling via LDAP, Open Authorization (OAuth), or SSO authentication and isolates computing resources to ensure reproducibility and stability. Real-world deployment demonstrates the effectiveness of the architecture in simplifying GPU access, improving performance isolation, and supporting secure, multi-tenant environments in educational institutions. The proposed design bridges the gap between pedagogical innovation and infrastructure resilience, paving the way for scalable and maintainable development.