Mia AI Lab's sparkDash Watches Your Entire NVIDIA DGX Spark Fleet at Once
sparkDash is an open-source React and Express dashboard that streams live GPU, memory, and LLM metrics from any number of NVIDIA DGX Spark machines in one browser tab.
- sparkDash is a real-time web dashboard for monitoring multiple NVIDIA DGX Spark GB10 units.
- Streams GPU, CPU, unified memory, storage, network, and local LLM metrics over WebSocket.
- Auto-detects llama.cpp, vLLM, and sglang backends, reporting live tokens per second.
- Add, edit, reorder, or remove Sparks from the UI with no process restart required.
- Ships as privileged ARM64 Docker container; SSH passwords encrypted with AES-256-GCM.
- MIT licensed; API is unauthenticated so run only on a trusted LAN.
sparkDash monitors multiple DGX Sparks from one dashboard
sparkDash consolidates GPU, CPU, memory, network, storage, and LLM throughput data from multiple NVIDIA DGX Spark systems. Mia AI Lab’s v2.0 release replaces per-machine terminal checks with a real-time web interface where operators can add, rename, reorder, and remove monitored systems without restarting the service.
The MIT-licensed project combines a React 19 single-page application with an Express 5 server. It runs as a privileged ARM64 Docker container and mounts the host’s /proc, /sys, and nvidia-smi resources to collect hardware metrics. The v2.0 code is available on the main branch.
JSON becomes the fleet map
Each DGX Spark is represented by a record in config/sparks.json. The interface updates that file, and the server adjusts its active monitors while running. Adding another machine requires its connection details rather than application changes or a separate collector implementation.
Local and remote machines use the same SparkMonitor, SystemCollector, and LlmProbe components. Local collection reads host data through mounted files and nvidia-smi, sometimes using nsenter to execute inside the host’s namespaces. Remote collection sends the equivalent commands through a shared sshExec() helper, using an SSH agent or sshpass.
Both collection paths produce the same data model, which keeps dashboard behavior consistent as systems are added or reordered.
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