Chunk 68.2

This chunk delivered the **monitoring, quality, and documentation layer** of the deployment. First, the assistant set up a full Prometheus + Grafana stack from scratch (no Docker, binaries on Ubuntu 24.04), configured it to scrape the prefill, decode, and router metrics endpoints, and provisioned an initial KV-cache dashboard. When the user asked for more metrics, the assistant expanded the dashboard to 17 panels covering prefill throughput, TTFT/TPOT latency percentiles, PD-disagg transfer speed and queue depths, cache hit rate, and request rates — all verified populating under live load. Second, the assistant systematically resolved the agent-coherence and tool-calling failures that had plagued the deployment. The root cause was a harness-side mismatch: the harness used text-based XML tools (`<write_to_file>`) instead of native OpenAI function-calling, which DeepSeek-V4 doesn't handle reliably. Fixes included dropping the wrong `--chat-template` override (restoring the native `encoding_dsv4` path), enabling thinking by default via `SGLANG_DEFAULT_THINKING=true` and a 7-line patch to `serving_chat.py`, setting the model name to `deepseek-v4-flash` so harnesses auto-detect the correct tool format, and setting temperature 0.6 as the server default to prevent greedy-decoding degeneration. The assistant also confirmed that the fp8 KV cache is intrinsic to the dsv4 backend and not causing the coherence issues. Finally, the assistant wrote a comprehensive engineering report (`DSV4_SM120_REPORT.md`) documenting the entire optimization journey: the ~17× throughput breakthrough, the MMA/kernel campaign, the PD-disagg deployment, the monitoring stack, and all the quality fixes — leaving the NextN-MTP and O(actual)-topk items as open follow-ups.

The Production Layer: Monitoring, Quality, and Documentation in the DeepSeek-V4-Flash Deployment 2355 words

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