End-to-End Observability for vLLM and TGI: from DCGM to Tokens
The article discusses the complexities of running large language model inference servers and the gaps in standard observability tools. It emphasizes the need for a layered observability approach that correlates various signals from different layers of the system. Key insights include the importance of understanding latency metrics and the KV cache as a critical bottleneck in performance monitoring.
- ▪Standard observability tools do not adequately cover the unique challenges of large language model servers.
- ▪Latency is multifaceted, with different metrics telling distinct stories about performance.
- ▪The KV cache is identified as the primary bottleneck, necessitating deeper insights into hardware interactions.
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Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/samuel_desseaux_815f9c463/end-to-end-observability-for-vllm-and-tgi-from-dcgm-to-tokens-4fbj |
| Publication time | Thu, 21 May 2026 11:37:13 +0000 |
| Retrieval time | 2026-05-21T11:51:11.072Z |
| Last seen | 2026-05-21T11:51:11.072Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ga75Hof4bkbE |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3943733) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } samuel desseaux Posted on May 21 End-to-End Observability for vLLM and TGI: from DCGM to Tokens #sre #observability #llm Running large language model inference servers in production exposes gaps that neither stock Prometheus dashboards nor the official documentation of vLLM or TGI cover completely. This article maps the layers that matter, names the exact signals to scrape and flags the traps most teams only hit after real traffic arrives.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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