LLM Deployment Verification Path

DevOps & CI/CD · flowchart diagram · MIT

A structured flowchart detailing the comprehensive verification process for large language model (LLM) deployments, covering smoke tests, performance bench

Source: https://github.com/jiaran-king/MicroLM/blob/782ae02f10c14b484a317f22115a066b3b10b91d/Readme/%E9%A1%B9%E7%9B%AE%E5%85%A8%E6%99%AF%E5%9B%BE/00-%E5%85%A8%E6%B5%81%E7%A8%8B%E5%88%86%E6%9E%90%EF%BC%88%E8%AE%AD%E7%BB%83%E3%80%81%E6%8E%A8%E7%90%86%E3%80%81%E8%AF%84%E6%B5%8B%E4%B8%8E%E9%83%A8%E7%BD%B2%EF%BC%89.md
Curated by jiaran-king
LLM Deployment Verification vLLM Benchmarking Smoke Tests CI/CD

Mermaid source

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flowchart TB
    V1["vLLM service"] --> V2["smoke tests<br>health · simple chat · IE · multi-turn · response_format"]
    V2 --> V3["benchmark<br>TTFT · tok/s · 并发吞吐 · 错误率"]
    V3 --> V4["stability check<br>normal vs constrained json_object"]
    V4 --> V5["部署结论<br>可用性 · 性能 · 稳定性"]

    classDef dep fill:#f5f3ff,stroke:#8b5cf6,color:#0f172a;
    classDef out fill:#f0fdf4,stroke:#22c55e,color:#0f172a;
    class V1,V2,V3,V4 dep;
    class V5 out;

What this diagram shows

This flowchart illustrates a robust verification pipeline for deploying Large Language Models (LLMs) using vLLM. It begins with basic service health and functionality checks via smoke tests (simple chat, information extraction, multi-turn conversations, response format validation). This is followed by performance benchmarking, measuring Time To First Token (TTFT), tokens per second (tok/s), concurrent throughput, and error rates. The final technical step is a stability check, comparing normal vs. constrained JSON object outputs to ensure consistent structured responses. The process culminates in clear deployment conclusions regarding availability, performance, and stability.

When to use it

Use this diagram when deploying a new LLM service, updating an existing one, or establishing a robust CI/CD pipeline for LLM applications. It's particularly useful for ensuring the quality, performance, and reliability of LLM services, especially those requiring structured output or operating under high concurrency.

How to adapt it for your project

This path can be adapted by customizing smoke test scenarios to specific application use cases, adding more detailed performance metrics relevant to your service's SLA, or integrating A/B testing for different model versions. The stability check can be extended to cover other output formats or complex interaction patterns. It can also be integrated into automated deployment pipelines.

Key concepts