AI & Machine Learning · Inference
Manage every on-prem model from one cockpit: flip between Overview and Deployed Models, watch GPU signals from DCGM, correlate CPU and memory with cluster pods, and track inference throughput, latency, tokens, and network paths serving traffic. When you are ready to ship, choose Deploy Existing (reuse an attached cluster) or Deploy New (stand up fresh resources)—without losing Prometheus or optional Ollama exporter telemetry.
Every frame below is a real Cloud Admin screen—paired with plain-language context so you know what you are looking at and why it matters operationally.
Your starting point for on-prem LLM models: KPI cards and charts summarize fleet health, including on-prem llm models overview with gpu and inference metrics. Spot drift early, then drill into the tab that explains the root cause.
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The deploy wizard’s Cluster step is where operators confirm llm deploy wizard cluster step Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
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The deploy wizard’s Model step is where operators confirm llm deploy wizard model selection Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
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The deploy wizard’s Deployment step is where operators confirm llm deployment configuration step Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
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The deploy wizard’s Resources step is where operators confirm llm resource and gpu allocation Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
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The deploy wizard’s Storage step is where operators confirm persistent storage for llm runtime Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
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The deploy wizard’s OpenWebUI step is where operators confirm openwebui configuration step Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
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The deploy wizard’s Service step is where operators confirm llm service exposure step Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
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When GPUs, tokens, and latency share one Overview, AI platform teams rehearse capacity instead of guessing during spikes.
Missing exporters surface as explicit signals—no synthetic token rates.
Existing vs net-new paths keep brownfield and greenfield teams on rails.
Pods, GPUs, and network tubes tie back to model serving—not disconnected dashboards.
Operate LLM Models from Cloud Admin alongside Kubernetes, observability, and delivery workflows.
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