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vLLM-Omni Performance Monitoring Dashboard: A Daily Trend Visualization Solution for Multimodal Models Across Hardware Platforms

A static performance monitoring dashboard project based on GitHub Pages, focusing on visualizing the daily performance trends of the vLLM-Omni multimodal model across different hardware platforms, using a pure Git workflow for data synchronization and deployment

vLLM多模态模型性能监控GitHub PagesAstroEChartsNVIDIAAMD昇腾AI 基础设施
Published 2026-05-24 06:42Recent activity 2026-05-24 06:47Estimated read 5 min
vLLM-Omni Performance Monitoring Dashboard: A Daily Trend Visualization Solution for Multimodal Models Across Hardware Platforms
1

Section 01

[Introduction] vLLM-Omni Performance Monitoring Dashboard: A Cross-Hardware Multimodal Model Performance Trend Visualization Solution

The open-source project vllm-omni-nightly-perf introduced in this article is a static performance monitoring dashboard based on GitHub Pages, focusing on visualizing the daily performance trends of the vLLM-Omni multimodal model on hardware platforms such as NVIDIA, AMD, and Ascend. The project uses a pure Git workflow for data synchronization and deployment, addressing the pain points of cross-hardware performance monitoring and providing intuitive time-series charts to display performance evolution.

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Section 02

Project Background and Motivation

vLLM-Omni is a multimodal inference engine, but hardware diversity (e.g., NVIDIA A100/H100/H20, AMD MI300X, Huawei Ascend NPU) makes performance monitoring challenging. Existing Markdown tables lack time-dimensional visualization, making it impossible to quickly identify trends and comparisons. This project fills the gap by converting flat tables into time-series charts.

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Section 03

Core Design Philosophy

  1. Pure Git Workflow: Based on Git and GitHub Actions, no external dependencies, data changes are transparent and traceable;
  2. Graceful Degradation Strategy: Fault-tolerance mechanism ensures that PR data failures or anomalies do not break the page;
  3. Hardware Comparison Perspective: "Model × Hardware" time-series display for intuitive comparison of performance differences and trends.
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Section 04

Technical Architecture Analysis

Three GitHub Actions workflows collaborate:

  • Data Synchronization Layer: Daily pull of upstream performance/PR data, validation, then submission;
  • Site Construction Layer: Built with Astro + Tailwind + ECharts, deployed to GitHub Pages;
  • CI Layer: Runs code checks, unit tests, and end-to-end tests during PRs.
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Section 05

Data Model Design

  1. Identity Mapping Layer: Stable IDs decouple upstream naming changes to maintain data consistency;
  2. Performance Time Series: Organize metrics (pass rate, P99 latency, etc.) by date/hardware;
  3. PR Attribution System: Three methods (direct, inferred, platform) to link code changes with performance variations.
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Section 06

Visual Interface Design

  1. Homepage: Grid of model cards, including today's pass rate, 7-day changes, and mini SVG line charts;
  2. Details Page: Switchable metrics, ECharts interactive charts, time range selection, and alarm markers;
  3. About Page: Transparent disclosure of data sources, thresholds, and attribution limitations.
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Section 07

Limitations and Future Directions

Limitations: No Pareto curves, no sub-daily granularity, no cost metrics, no user customization; Future: Add JSON data sources, cross-model comparisons, cost estimation, custom domain names, etc.

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Section 08

Practical Insights and Conclusion

Insights: Data source decoupling, Git as the source of truth, progressive visualization, honest attribution; Conclusion: Minimalist tech stack solves practical problems, provides reference for multimodal teams, welcome to follow during the pre-implementation phase.

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