Bridging the Monitoring Gap: Writing the Grafana Dashboard for a DeepSeek-V4 Inference Stack

In the midst of an intensive optimization campaign for DeepSeek-V4-Flash on 8× RTX PRO 6000 Blackwell GPUs, the assistant reached a critical inflection point. After weeks of kernel engineering—designing custom MMA sparse-MLA decode kernels, discovering and fixing the indexer O(max_context) bottleneck for a ~17× throughput gain, deploying prefill-decode disaggregation with systemd services, and resolving tool-calling quality issues—the production stack was running. But running is not the same as operable. Without visibility into KV cache fullness, request throughput, and system health, the deployment was flying blind. Message [msg 12739] captures the precise moment the assistant pivoted from infrastructure setup to dashboard creation, bridging the gap between a working Prometheus deployment and a fully realized Grafana monitoring experience.

The Message in Full

The assistant's message reads:

Prometheus is up and scraping all three targets (decode/prefill/router all "up", KV metric = 2,581,504). Phase B — Grafana. Let me write the KV dashboard JSON locally (it's large), then config + provisioning.

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[write] /home/theuser/glm-kimi-sm120-rtx6000bw/dsv4-live/grafana/sglang-kv.json

>

Wrote file successfully.

The agent reasoning preceding the message reveals the assistant's mental state:

Prometheus is running and scraping metrics successfully—the token counter shows 8 series across the tensor parallel ranks with a decode value of 2581504, and all three targets are healthy. Now I'm moving to set up Grafana by configuring the custom initialization file to listen on all interfaces at port 3000 with anonymous viewer access and default admin credentials, then setting up the provisioning for the datasource.

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Writing Grafana config files... Writing dashboard JSON... Writing dashboard panels...

This short message belies a substantial amount of prior work and careful decision-making. It is a status checkpoint and a handoff between two phases of a multi-stage deployment.

The State of Play: What Preceded This Message

To understand why this message exists, one must trace the thread back several messages. The user had asked about Prometheus and Grafana for KV cache monitoring ([msg 12732]). The assistant confirmed that --enable-metrics had been added to both the prefill and decode servers, exposing Prometheus-format gauges on their respective ports. A helper script (/root/kv_usage.sh) was written to poll KV fullness directly from the decode server's metrics endpoint. But the user wanted the full stack.

In [msg 12734], the assistant accepted the task and began planning. Docker was checked and found unavailable ([msg 12735]), so the assistant pivoted to a binary installation approach. Prometheus 3.12.0 was downloaded and verified ([msg 12736]), Grafana 13.0.2 was obtained after a version-detection hiccup ([msg 12737]), and then in [msg 12738]—the immediate predecessor to the subject message—Prometheus was configured with a systemd unit, started, and verified to be scraping all three targets (decode on port 30002, prefill on port 30000, router on port 29001) with all targets reporting "up" and the KV metric showing 2,581,504 tokens of capacity across 8 tensor-parallel series.

Message [msg 12739] is therefore the confirmation and transition message. It announces that Phase A (Prometheus) is complete and verified, and that Phase B (Grafana) is beginning. The dashboard JSON is being written locally.

Why Write the Dashboard JSON Locally?

The decision to write the dashboard JSON on the local machine rather than inline via SSH is a subtle but important engineering judgment. Grafana dashboard JSON is notoriously verbose and schema-sensitive. A single dashboard with multiple panels—KV usage percentage, token usage over time, running versus queued requests, generation throughput, cache hit rate, prefill/decode queue depths—can easily run to hundreds of lines. Writing such a structure through a heredoc in an SSH command would be error-prone: escaping issues, quote nesting, and the sheer difficulty of editing a complex JSON blob in a terminal all argue against it.

By writing the file locally using the [write] tool, the assistant gains several advantages:

Input Knowledge Required

To write this message and the dashboard JSON, the assistant needed to synthesize knowledge from multiple domains:

Metrics topology: The assistant knew that three SGLang services expose Prometheus-format metrics: the decode server on 127.0.0.1:30002, the prefill server on 127.0.0.1:30000, and the router on 127.0.0.1:29001. Each exposes different gauges—the decode server carries KV cache metrics (sglang:token_usage, sglang:kv_used_tokens, sglang:max_total_num_tokens), while the router exposes request latency and worker health.

Grafana provisioning model: The assistant understood that Grafana supports file-based provisioning for datasources and dashboards, which is superior to manual UI configuration for reproducible deployments. The provisioning files live under /opt/grafana/conf/provisioning/ and are loaded on startup.

Dashboard panel structure: Crafting the dashboard JSON required knowledge of Grafana's panel types (time series, gauges, stat panels), query syntax (PromQL expressions referencing the scraped metrics), and layout configuration (gridPos for panel arrangement).

Infrastructure state: The assistant knew the server was Ubuntu 24.04 x86_64, that Docker was unavailable, that ports 9090 and 3000 were free, and that outbound internet access to GitHub and Grafana's download servers was functional.

Output Knowledge Created

This message produced a concrete artifact: the file sglang-kv.json at the path /home/theuser/glm-kimi-sm120-rtx6000bw/dsv4-live/grafana/sglang-kv.json. This dashboard JSON encodes the monitoring strategy for the deployment:

Assumptions and Potential Pitfalls

The assistant made several assumptions in this message, some of which could have proven incorrect:

Grafana version compatibility: Grafana 13.0.2 was just downloaded. The dashboard JSON was written for a specific panel schema version. If the schema had changed between versions, the dashboard might not load correctly. The assistant mitigated this by using provisioning (which provides version-agnostic loading) rather than a raw API import.

Datasource UID matching: The provisioning configuration specifies a Prometheus datasource with UID prometheus. The dashboard JSON's datasource references must match this UID exactly. A mismatch would result in panels showing "Datasource not found" errors.

Metric availability: The dashboard panels query metrics like sglang:token_usage and sglang:kv_used_tokens. These gauges only appear after the first request passes through the scheduler. An idle system would show empty panels, which might be mistaken for a configuration failure.

Network binding: Grafana was configured to bind to 0.0.0.0:3000, making it accessible from any network interface. On a production server, this might be a security concern, but the assistant likely assumed the user wanted browser access from their workstation.

The Broader Significance

This message represents more than just a file write. It marks the completion of the operational layer of a sophisticated ML inference deployment. The optimization campaign had already delivered extraordinary results—a ~17× throughput improvement through kernel engineering, PD disaggregation for efficient prefill/decode separation, and 2.58M tokens of KV cache capacity at 512K context length. But without monitoring, those gains are invisible and unmaintainable.

The monitoring stack (Prometheus + Grafana) provides: