dcgm

GPU Cluster Monitoring (GCM): Large-Scale AI Research Cluster Monitoring

GPU Observability with workload attribution. One OTLP agent per node ties hardware metrics (NVIDIA, AMD, Intel Gaudi) to the K8s pod or Slurm job burning the GPU.

Simulate NVIDIA GPUs for testing. 7 behavior profiles, scale to 1000+ GPUs, Docker-ready Prometheus exporter using DCGM

FakeAI: Rapid Development and Testing for AI Infrastructure

Automated acceptance toolkit for Linux deep learning GPU servers

GPU-native agent-swarm orchestration for the NVIDIA AI stack — NeMo, NIM, Triton, DCGM, NGC, NIXL, OpenShell. Spawn GPU-pinned agent teams across DGX/HGX nodes with NVLink-aware scheduling, task DAGs, adaptive scheduling, and full observability.

Open-source GPU dynamic power management for datacenter — Python brain, Rust agent, Prometheus/Grafana

GPU workload analyzer for AI infra teams on Kubernetes — semantic problem detection beyond raw metrics

AI cluster debugging lab for distributed LLM and HPC workloads: GPU, NCCL, Kubernetes, failure analysis, and tuning recommendations.

Production-grade health monitoring and predictive fault management system for NVIDIA A100/H100 GPU fleets

Production LLM serving infrastructure using Triton Inference Server, vLLM, and Ray Serve with OpenAI-compatible endpoints. Includes Kubernetes autoscaling configs driven by DCGM GPU metrics and a BentoML packaging path for portable model deployment.

Local NVIDIA AI Factory operations copilot lab with RAG, hosted NIM, LangGraph, K3s, GPU validation, DCGM, Prometheus, Grafana, approvals, and audit logging.

Free one-shot NVIDIA GPU cluster health check — spot idle-but-allocated GPUs, thermal/ECC/VRAM risks, and $ wasted on idle GPUs. Zero dependencies.