Solutions · Architecture · Cluster Management · MLOps
AI Factory
Comprehensive real-time monitoring of AI system resource utilization.
UtilizationSystem healthOrchestrationKubernetesSlurm
01 — Utilization
Every GPU accounted for.
Utilization, system health and container orchestration in one operational view — the same telemetry that backs the SLA.
Fig. 01 — GPU resource utilization
GPU-053.2%Whisper · inference
GPU-178.3%Stable Diffusion · generating
GPU-291.0%LLaMA · training
Utilization, thermal state, network throughput and storage I/O are tracked per node; container orchestration status is reported alongside.
02 — Orchestration
Containers, allocations, scheduling.
Each service is scheduled onto a named accelerator, with CPU and memory reservations recorded alongside.
| Container ID | Service name | CPUreservation | Memoryreservation | GPU allocation |
|---|---|---|---|---|
| whisper-001 | Whisper Service | 15% | 8GB | GPU-0 |
| sd-002 | Stable Diffusion | 25% | 12GB | GPU-1 |
| llama-003 | LLaMA Training | 35% | 16GB | GPU-2 |
| rag-004 | RAG Engine | 20% | 10GB | N/A |
Scheduling is handled by Kubernetes and Slurm.Per-node telemetry is retained and available on request.
Request a Custom Deployment Consultation
Our technical specialists will develop a tailored implementation plan with comprehensive support