LLM Deployment Verification Path

ML & AI · flowchart diagram · MIT

This flowchart illustrates a comprehensive verification path for deploying Large Language Models (LLMs), covering smoke tests, performance benchmarks, and

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 Benchmarking Stability vLLM AI

Mermaid source

%%{init: {"theme": "base", "themeVariables": {"background": "#ffffff", "primaryColor": "#f5f3ff", "primaryBorderColor": "#8b5cf6", "primaryTextColor": "#0f172a", "lineColor": "#64748b"}}}%%
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

The diagram outlines a sequential process for validating an LLM service deployment. It starts with basic smoke tests for health, chat, information extraction, multi-turn conversations, and response format. This is followed by performance benchmarking, measuring metrics like TTFT, tokens per second, concurrent throughput, and error rates. Finally, a stability check assesses structured output consistency, comparing normal versus constrained JSON object responses, leading to a final deployment conclusion on availability, performance, and stability.

When to use it

Use this diagram when deploying a Large Language Model (LLM) into a production environment to ensure its availability, performance, and the integrity of its structured outputs. It's crucial for validating model behavior and stability before go-live.

How to adapt it for your project

This path can be adapted by adding more specific domain-related tests to the smoke test phase, integrating A/B testing or canary deployments, or incorporating security and compliance checks. The benchmark metrics can be customized based on application-specific SLAs, and the stability checks can be extended to cover other output formats or complex reasoning tasks.

Key concepts