Chunk 70.2

In this chunk, the assistant diagnosed a production incident where the cluster became unresponsive under load, returning `KVTransferError` aborts. By examining the prefill logs, the assistant identified the root cause: the single prefill server's unbounded queue had accumulated ~20 requests and ~220K pending tokens under a load burst, causing time-to-first-token to balloon to minutes, clients to abort, and in-flight KV transfers to fail. The assistant implemented admission control by adding `--max-queued-requests 32` to both serve scripts, preventing unbounded pileup. It also attempted to add HiCache (hierarchical caching) for prefix reuse and VRAM relief, but hit a configuration error: DeepSeek V4 requires `--hicache-ratio` instead of `--hicache-size`. After fixing that, HiCache was enabled with ratio 2.0, allocating ~20 GB of host cache on the prefill worker. The assistant then built a lightweight GPU exporter using pynvml (deployed as a systemd service scraping 8 GPUs) and added it to Prometheus. It extended the Grafana dashboard generator with a node-health row (service status, prefill queue depth, decode KV usage, GPU memory/utilization) and a HiCache row (host token usage, capacity, cache hit rate). A Grafana permission issue—the dashboard was uploaded to the General folder, but anonymous access was scoped only to the `sglang` folder—was fixed by re-uploading with the correct `folderUid`. The user confirmed Grafana was working but then reported that decode was stuck again, prompting the assistant to begin a fresh diagnostic round (checking service states, queues, GPU memory, and recent errors). The overarching themes are systematic production debugging (tracing a stuck cluster to queue saturation), layered fixes (admission control + HiCache), and building observability infrastructure (GPU exporter, Grafana panels) to prevent future blind spots.

From Queue Saturation to Sparse Attention: A Production ML Debugging Marathon 2993 words

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