Grafana for LLM stacks — what you must chart before you blame the GPU.
SRE pasted a Kubernetes-style Grafana board; infra green—yet Arabic QA saw multi-second slowdowns once KV cache pressure fragmented batches [1][2].
Pair Prometheus scraping with labelled inference jobs—vLLM exposes service counters worth wiring first [3][4]. This mirrors grids we cite in Nuqta weekly RAG scorecard and the journal hub.
Five layers before another vendor UX debate.
Edge HTTP/error tier, GPU batch tier, KV memory tier from generation, retrieval quality tier from RAG, finally finance tier reconciling USD-per-million tokens [2][5].
If finance screams before retrieval does, odds are blaming the frontier model for a chunked-index failure—read five RAG metrics.
Concrete metrics operators copy this week.
Request histograms comparing client RTT versus container latency, MCP tool-call counters whenever agents widen scope [6]; Grafana documents panel discipline [1].
One wall that aligns latency, people, citations, dollars ends GPU shopping debates prematurely.
Stack ladder diagram.
Oman / Gulf compliance footnote.
PDPL logging duties still apply to observability teams—treat log access like data access [7].
Frequently Asked Questions.
- Is vendor dashboard enough? Enough for SKU checks, rarely for hidden inference queues [3].
- First KPI? Stable-context p95 end-to-end [1].
- Where belongs RAG? Retrieval latency drift—five metrics article.
- Prometheus pairing? Grafana visualizes Prometheus series per Grafana Labs canonical architecture [2].
- Weekly rhythm? Freeze screenshots into Nuqta scorecard [8].
Sources.
[1] Grafana Labs — Dashboard best practices.
[2] Prometheus — Metric types.
[3] vLLM docs — Observability.
[4] NVIDIA — H100 platform brief.
[5] Google SRE Book — SLI/SLO framing.
[6] Anthropic — Model Context Protocol docs.
[7] Sultanate of Oman — PDPL supervisory guidance packs (consult counsel).
[8] Nuqta — Grafana templates synced to RAG ops scorecard, May 2026.
Related posts
- What is vLLM — and why production teams use it.
vLLM is an open inference engine for LLMs: scheduling, continuous batching, and KV memory designs such as [PagedAttention](/en/journal/what-is-pagedattention-llm-serving-2026). The point is not a thin API wrapper — it is raising useful throughput under real traffic [1].
- Five RAG metrics to check before you blame the LLM.
Before you raise model spend or switch vendors, measure retrieval, chunks, and escalation. Most production hallucination starts in documents and indexes — not parameter count.
- The weekly RAG scorecard before blaming the frontier model.
Four KPIs — recall@k, citation accuracy, p95 latency, drift — stamped every Monday keeps retrieval honest.
- What Is KV Cache in LLM Inference and How Does It Eat VRAM?
The GPU is not the whole truth — part of inference speed is reusing intermediate keys and values instead of recomputing layers for every token.
- Inference vs training for LLMs — who pays for what.
Training might run once (or for many hours) and you pay a cluster bill. Inference runs forever and turns a model into a per-token Opex line. This article separates the two checkbooks so pilot budgets are not mixed with product bills [1].
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