Zero-Cloud AI Code Review: Connecting Self-Hosted GitLab to Local CUDA LLM Runtimes

Описание и применение

Пайплайн AI‑обзора кода без облака, использующий локальный GPU и GitLab Self‑Managed, обеспечивает мгновенную проверку безопасности и отсутствие исходящего трафика.

Задача

Enterprise teams cannot send proprietary source code to third‑party cloud APIs due to regulatory compliance, yet they need AI‑assisted code reviews.

Результат

The pipeline achieves sub‑microsecond inference, zero data egress, and instant security feedback within seconds of a merge request.

Рабочий процесс

• 1. Push feature branch to self‑hosted GitLab
• 2. GitLab triggers Merge Request webhook
• 3. Local AI Review Worker receives diff via GitLab API
• 4. Worker runs optimized C++20/CUDA inference on RTX GPU
• 5. Worker returns security audit and code suggestions
• 6. GitLab CI job posts review as MR note via GitLab REST API

Доказательства

For enterprises operating under strict regulatory compliance (finance, healthcare, defense, or proprietary IP vaults), sending proprietary source code to third-party multi-tenant cloud APIs is an absolute dealbreaker.

Источник

All data remains strictly within the local area network (LAN) or on-premise workstation cluster

Источник

time-to-first-token (TTFT) for a 4,000-token git diff is under 80ms, running entirely offline with zero network packets leaving the local machine

Источник

We configure an on-premise GitLab Runner tagged with local-gpu to intercept merge request diffs, format a code-audit prompt, and send it to our local inference endpoint

Источник