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miaai-lab/sparkdash

Analysis updated 2026-05-18

79TypeScriptAudience · ops devopsComplexity · 4/5Setup · moderate

TLDR

A web dashboard to monitor NVIDIA DGX Spark AI computers in real-time, showing live GPU, CPU, and local AI model performance metrics. You can manage multiple machines and run model benchmarks from a single browser window.

Mindmap

mindmap
  root((sparkDash))
    What it does
      Live metrics streaming
      Manage multiple Spark units
      Run AI model benchmarks
      Remote SSH connections
    Monitors
      GPU and CPU usage
      Memory and storage
      Network activity
      Local AI model speed
    Security and Control
      Encrypted SSH passwords
      Role assignment
      Graceful shutdowns
      Wake-on-LAN commands
    Tech stack
      React and TypeScript
      Express and WebSockets
      Docker container
      Multiple themes
    Use cases
      Monitor local AI models
      Benchmark language models
      Manage remote AI computers
      Test concurrent prompts

Code map

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What do people build with it?

USE CASE 1

Monitor live performance metrics for multiple NVIDIA DGX Spark computers from a single browser window.

USE CASE 2

Benchmark local AI language models to measure response speed under different levels of concurrent requests.

USE CASE 3

Remotely manage and shut down AI machines over secure SSH connections without physical access.

USE CASE 4

Track how fast AI models generate text by detecting common serving software automatically.

What is it built with?

TypeScriptReact 19Express 5WebSocketsDocker

How does it compare?

miaai-lab/sparkdashalpha-vector-core/polymarket-arbitrage-trading-botalpha-vector-core/polymarket-trading-bot
Stars797979
LanguageTypeScriptTypeScriptTypeScript
Setup difficultymoderatemoderatemoderate
Complexity4/54/54/5
Audienceops devopsdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Runs in a single Docker container but requires one or more NVIDIA DGX Spark machines and SSH access details to be useful.

No license information was provided in the explanation.

In plain English

sparkDash is a web dashboard for monitoring one or more NVIDIA DGX Spark machines in a single browser window. The DGX Spark is a small but powerful ARM64 computer built around NVIDIA's GB10 chip, designed for running local AI models. sparkDash streams live metrics from these machines, covering GPU usage, CPU load, memory, storage, network activity, and local language model performance. You can add, edit, reorder, or remove Spark units directly from the interface without restarting anything. The main features center on real-time visibility and control. A shared Overview page gives you a bird's-eye look at all connected Sparks, while each unit gets its own tabbed detail page. The dashboard can connect to Sparks running locally or remotely over SSH, using either a key file or a password. For language model workloads, it automatically detects common serving software like llama.cpp, vLLM, or sglang, and reports live tokens per second. It also includes a prompt showcase mode that streams up to 32 concurrent prompts across multiple terminals, and a decode benchmark for measuring throughput under different concurrency levels. Security and configuration are handled with care. SSH passwords are encrypted using AES-256-GCM and are never written to the main config file or returned through the API. The system supports assigning roles to each Spark, such as Head, Worker, or Standalone, with worker units linking back to their head node. You can also trigger graceful shutdowns or Wake-on-LAN power commands, and batch actions are available from the Overview page. The project is built with React 19 and TypeScript on the frontend, running on Express 5 with WebSockets on the backend. It is designed to run inside a single Docker container, which is the recommended way to deploy it. You can also run it in a development mode with hot reloading. Four themes are available, and your choice persists across sessions. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
Help me set up sparkDash in a Docker container to monitor my NVIDIA DGX Spark machine over SSH.
Prompt 2
Create a configuration for sparkDash to connect to a remote DGX Spark unit using an SSH key file instead of a password.
Prompt 3
Show me how to use the sparkDash decode benchmark to measure local AI model throughput with 32 concurrent prompts.
Prompt 4
Explain how to assign Head, Worker, and Standalone roles to my DGX Spark units in sparkDash.

Frequently asked questions

What is sparkdash?

A web dashboard to monitor NVIDIA DGX Spark AI computers in real-time, showing live GPU, CPU, and local AI model performance metrics. You can manage multiple machines and run model benchmarks from a single browser window.

What language is sparkdash written in?

Mainly TypeScript. The stack also includes TypeScript, React 19, Express 5.

What license does sparkdash use?

No license information was provided in the explanation.

How hard is sparkdash to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is sparkdash for?

Mainly ops devops.

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