gpu-hot
GPU Hot is a lightweight, web-based real-time monitoring dashboard for NVIDIA GPUs. It runs as a self-hosted Docker container and supports single-machine or multi-node cluster monitoring with sub-second metric updates.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | psalias2006/gpu-hot |
| Owner | psalias2006 |
| Primary language | JavaScript |
| License | MIT — OSI-approved |
| Stars | 1.6k |
| Forks | 78 |
| Open issues | 6 |
| Latest release | v1.9.0 (2026-05-28) |
| Last updated | 2026-06-13 |
| Source | https://github.com/psalias2006/gpu-hot |
What gpu-hot is
FastAPI backend with NVML GPU monitoring and WebSocket streaming; frontend uses Chart.js for historical visualization. Supports NVIDIA SMI fallback for older GPUs, multi-node hub aggregation, and per-process metrics collection.
Get the gpu-hot source
Clone the repository and explore it locally.
git clone https://github.com/psalias2006/gpu-hot.gitcd gpu-hot# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- NVIDIA Container Toolkit required; verify driver compatibility and GPU access inside containers before deployment.
- Process monitoring requires `--pid=host` and `--init` flags, which grant container access to host process namespace—evaluate in security-sensitive environments.
- Multi-node hub mode relies on HTTP connectivity between nodes; ensure firewall rules allow port 1312 and monitor network latency for aggregation delay.
- Metrics are stored in-memory only; implement external scraping (e.g., Prometheus `/api/gpu-data` endpoint) if long-term retention is needed.
- UPDATE_INTERVAL tuning affects CPU usage during idle periods; default 0.5s is aggressive for many-GPU setups; increase if CPU cost is a concern.
When to avoid it — and what to weigh
- Requires managed SaaS monitoring — GPU Hot is self-hosted only. If your organization requires vendor-managed monitoring with SLAs, incident escalation, or integration with managed observability stacks, evaluate hosted alternatives.
- Non-NVIDIA GPU environments — Designed for NVIDIA GPUs only (via NVML/nvidia-smi). AMD, Intel, or other accelerator types are not supported.
- Critical alerting and automation — No native alerting, webhooks, or integration with incident management systems. Not suitable as the primary monitoring tool if you need automatic escalation or remediation.
- Complex data retention and compliance — In-memory historical data only; no built-in persistence layer or compliance-focused audit logging. Projects with retention or regulatory reporting requirements should extend with a metrics backend.
License & commercial use
MIT License. Permissive OSI license allowing commercial use, modification, and redistribution with attribution and liability disclaimer.
MIT permits commercial deployment and bundling without royalties. Verify compliance with your internal policies regarding open-source use; include license text and attribution in distribution materials.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
Process monitoring via `--pid=host` exposes container to host process information; limit deployment to trusted networks and assess impact in multi-tenant environments. No authentication or TLS for dashboard or WebSocket; place behind reverse proxy with auth if exposed outside internal networks. In-memory metrics include process names and GPU usage, which may be considered sensitive in shared infrastructure.
Alternatives to consider
Grafana + Prometheus (with NVIDIA DCGM exporter)
Enterprise-grade stack with persistent storage, alerting, and multi-datasource dashboards. Higher operational complexity but better for large-scale and compliance-driven deployments.
NVIDIA DCGM + Kubernetes monitoring
Native NVIDIA solution integrated with Kubernetes; better job-scheduler correlation. Requires more infrastructure and NVIDIA licensing in some tiers.
Commercial platforms (Wiz, OctoML, Lambda Labs)
Managed SaaS with alerting, team access, and cloud-native integrations. Trade-off: external dependency, no self-hosting, vendor lock-in risk.
Build on gpu-hot with DEV.co software developers
GPU Hot is ideal for development and internal MLOps use. For enterprise deployments, compliance, alerting, or multi-cloud needs, discuss custom monitoring architecture with our DevOps specialists.
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gpu-hot FAQ
Can I use GPU Hot in a Kubernetes cluster?
Does GPU Hot persist metrics to disk?
What is the overhead of GPU Hot on GPU training jobs?
How do I secure the dashboard if deployed on a public network?
Software development & web development with DEV.co
Adopting gpu-hot is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source devops software in production.
Need Production GPU Monitoring?
GPU Hot is ideal for development and internal MLOps use. For enterprise deployments, compliance, alerting, or multi-cloud needs, discuss custom monitoring architecture with our DevOps specialists.