Sparky Command Center: Dependency-Free Monitoring for GPU Fleets and LLM Inference

Repositorytonyd2wild/The-Sparky-Command-CenterTool

Sparky Command Center is a lightweight, zero-dependency web dashboard designed to monitor DGX Spark fleets and custom Linux GPU rigs. It collects hardware metrics over SSH and scrapes live inference performance data from vLLM and llama.cpp without external databases.

Key points

  1. Zero dependencies: Operates on Python 3.8+ standard library and SSH, requiring no third-party packages, build steps, or external databases.
  2. Hardware telemetry: Visualises GPU utilisation, VRAM, power limits, live temperature sparklines, and host system CPU/sensor metrics.
  3. Inference performance: Scrapes Prometheus endpoints from vLLM and llama.cpp to display decode/prefill tokens per second, TTFT, KV cache usage, and concurrency splits.
  4. Token history and lane tracking: Features a cumulative token tracker resilient to server restarts, alongside optional ComfyUI lanes with peak-VRAM watermarks.

Why it matters

Setting up full observability stacks like Prometheus and Grafana for private GPU nodes is often heavy-handed. This project provides a single-page overview tailored specifically to LLM serving metrics and multi-GPU thermal limits.

Analysis

Sparky Command Center targets homelab enthusiasts, researchers, and small AI engineering teams operating dedicated inference clusters. By relying purely on standard Python, SSH, and JSON configuration, it eliminates setup overhead. With around 40 stars and 4 forks, it represents a relatively young, niche utility rather than an enterprise-grade observability suite. However, its fine-grained focus on concurrent token throughput and hardware thermals makes it a practical solution for local AI infrastructure.

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