The Hunt for KV Cache Metrics: A Detective Story in Production ML

In any complex production deployment, the gap between "it's running" and "we know how it's running" can be vast. This article examines a single message in a coding session — message index 12728 — where an AI assistant, deep in the process of deploying the DeepSeek-V4-Flash model on 8× RTX PRO 6000 Blackwell GPUs with prefill-decode disaggregation, pivots from assumption-driven searching to systematic discovery. The message is a turning point: the moment the assistant stops guessing where the metrics live and instead finds them.

The Context: A Deployment Without Visibility

The conversation leading up to this message tells a story of rapid, high-stakes engineering. The assistant had just completed a grueling optimization campaign for DeepSeek-V4-Flash — custom MMA sparse-MLA decode kernels, a 17× throughput breakthrough by fixing an O(max_context) indexer bottleneck, and deployment of prefill-decode (PD) disaggregation across 8 GPUs with systemd services. The PD setup was elegant: prefill on GPUs 0–3 (NUMA 0), decode with the custom MMA+indexer kernels on GPUs 4–7 (NUMA 1), and a router on port 30001 serving as the OpenAI-compatible endpoint. All three services were enabled at boot, the KV cache held 2.58 million tokens at 512K context length, and thinking + tool-calling had been validated end-to-end.

But the user, in message 12724, asked a simple question that exposed a blind spot: "do we have any metrcis / prometheus / grafana that we can use to see how full kv-cache is?" The answer was essentially "no." The router had a Prometheus endpoint on port 29001, but it only exposed request-level metrics — HTTP connections, inflight request age, worker health — nothing about KV cache fullness. The decode server, which held the actual KV pool, had metrics disabled entirely.

The False Start: Assumptions Lead to Dead Ends

The assistant's first response ([msg 12725]) was confident but built on an assumption. It reasoned that KV-cache utilization is a per-server gauge living on the decode server, and that enabling --enable-metrics would surface it. It queried the router's metrics endpoint (finding only smg_* request metrics), confirmed the decode server had no /metrics endpoint, and then searched for metric names in a specific file: /root/sglang-dsv4/python/sglang/srt/metrics/collector.py.

This file path was a guess. The assistant assumed the metrics collector lived under srt/metrics/, mirroring a common convention in Python projects. But the search returned nothing — the file didn't exist. Message 12726 doubled down on the same assumption, using a more specific grep pattern (name="sglang:[a-z_]+") against the same nonexistent file. Still empty. Message 12727 finally checked whether the file existed at all, and the answer was clear: file: with no output, confirming the path was wrong.

This sequence illustrates a classic debugging pitfall: when a search returns nothing, the first instinct is to refine the search, not question the search path. Three consecutive messages were spent fruitlessly probing a file that didn't exist, each time assuming the problem was with the grep pattern rather than the file location.

The Breakthrough: Systematic Discovery

Message 12728 is where the assistant breaks out of this cycle. The reasoning is explicit: "I need to locate the actual metrics collector file since the expected path doesn't exist." This is the critical insight — not "I need a better grep pattern" but "I need to find the right file first."

The assistant then executes a carefully constructed bash command that does three things in parallel:

  1. Finds all metrics-related Python files using find python/sglang -path "*metric*" -name "*.py" — a broad, recursive search that doesn't assume a particular directory structure.
  2. Greps for token/cache usage references across the entire srt directory — again, broad and recursive, ignoring the previous wrong path.
  3. Searches for gauge names containing usage/token — the same kind of search that failed before, but now applied to the entire python/sglang/srt/ tree rather than a single file. The results are immediate and revealing. The find command returns seven files, including python/sglang/srt/observability/metrics_collector.py — the actual metrics collector, living under observability/, not metrics/. Other files include forward_pass_metrics.py, request_metrics_exporter.py, and metrics_reporter.py in the scheduler components. The directory structure tells a story: sglang's observability infrastructure is more mature than the assistant assumed, with dedicated modules for forward-pass metrics, request metrics export, and a metrics collector — all organized under an observability/ namespace that the assistant hadn't considered.

What This Message Reveals About the Thinking Process

The reasoning in message 12728 is deceptively simple — just one sentence: "I need to locate the actual metrics collector file since the expected path doesn't exist." But this sentence represents a fundamental cognitive shift. The assistant had been operating under a mental model of the codebase that placed metrics in srt/metrics/collector.py. Three rounds of failed searches eroded that model, and this message is where the model is explicitly discarded and replaced with a discovery-oriented approach.

The bash command itself reveals the assistant's refined strategy. Rather than searching for a specific file by a guessed name, it uses find with a wildcard pattern to discover all metrics-related files. Rather than grepping for specific gauge names in a single file, it uses grep -rln (recursive, showing only filenames) across the entire srt/ directory to find any file referencing token usage or cache usage. The third sub-command — grep -rhoE "\"sglang:[a-z_]+\"" — is the most sophisticated: it extracts all Prometheus-style gauge name strings from the entire source tree, then filters for relevant keywords. This is a "show me everything you've got" approach, and it works.

Input Knowledge Required

To fully understand this message, a reader needs several pieces of context:

Output Knowledge Created

This message produces several concrete outputs:

  1. The correct file paths: The assistant now knows that metrics_collector.py lives at python/sglang/srt/observability/metrics_collector.py, not python/sglang/srt/metrics/collector.py. This is the key piece of information that unlocks the entire monitoring setup.
  2. The metrics file inventory: Seven metrics-related files are discovered, giving the assistant a map of the observability infrastructure.
  3. The foundation for the next steps: In the following message ([msg 12729]), the assistant uses this knowledge to find the exact gauge names (sglang:full_token_usage, sglang:kv_used_tokens, sglang:kv_available_tokens, sglang:max_total_num_tokens) and enable metrics on both servers.

Mistakes and Incorrect Assumptions

The primary mistake is the assumption about the file path. The assistant spent three messages (12725–12727) searching python/sglang/srt/metrics/collector.py — a path that simply didn't exist. This is a natural error: the word "metrics" appears in both the old and new paths, and the assistant's training data likely includes examples of sglang's older codebase structure where the metrics collector was in a different location.

A subtler mistake is the initial grep pattern. In message 12726, the assistant uses grep -rhoE "name=\"sglang:[a-z_]+\"" — looking for gauge names assigned via a name= parameter. But in Prometheus instrumentation libraries, gauge names are often specified differently (as positional arguments to Gauge() constructors, or as string literals in decorators). The pattern was too specific to the assistant's mental model of how gauges are defined.

Why This Message Matters

Message 12728 is a small but crucial pivot point. Before it, the assistant was stuck in a loop of refining searches against a nonexistent file. After it, the path to a working monitoring stack is clear. The very next message ([msg 12729]) uses the discovered file paths to extract the exact gauge names, and within a few more messages, the assistant has enabled metrics on both servers, created a helper script (kv_usage.sh) for live KV fullness monitoring, and offered to install the full Prometheus + Grafana stack — which the user accepts.

The message also demonstrates a valuable engineering principle: when your assumptions lead to dead ends, step back and search broadly before narrowing down. The find command in this message is the embodiment of that principle — it doesn't assume where the files should be; it asks the filesystem where they actually are.

Conclusion

In the grand narrative of this coding session — spanning custom CUDA kernels, 17× throughput improvements, and production deployment across 8 GPUs — message 12728 is a quiet but essential beat. It's the moment the assistant stops guessing and starts discovering. The KV cache metrics that the user wanted to monitor were always there, hidden in the observability/ directory. The only thing standing between the assistant and those metrics was an incorrect assumption about where to look. Correcting that assumption, through systematic file discovery rather than path guessing, turned a frustrating dead end into the foundation for a full monitoring stack — one that would eventually include a 17-panel Grafana dashboard tracking KV usage, throughput, latency percentiles, PD transfer speeds, and cache hit rates across the entire deployment.