The Metrics Gap: Investigating KV Cache Monitoring in a Production SGLang Deployment

Introduction

In the high-stakes world of large language model serving, observability is not a luxury—it is a necessity. When a production inference stack spans eight GPUs across two NUMA domains, with prefill-decode disaggregation, a custom MMA attention kernel, and a router balancing requests between servers, knowing what is happening inside the system is the difference between proactive management and reactive firefighting. This article examines a single message (message 12725) from an opencode coding session in which a user asks a simple but critical question: "do we have any metrics / prometheus / grafana that we can use to see how full kv-cache is?" The assistant's response—a blend of reasoning, reconnaissance, and technical exposition—reveals a common and consequential gap in production ML deployments: the difference between having some metrics and having the right metrics.

The Message

The assistant's response begins with an explicit reasoning trace, then delivers a conversational answer followed by a bash command to probe the live system:

## Agent Reasoning The user is asking whether we have monitoring set up to track KV cache utilization. Looking at the current setup, the sglang servers aren't exposing Prometheus metrics by default, though the router does have metrics enabled on port 29001. To monitor KV cache usage, I'd need to enable metrics on the sglang servers themselves. I need to check what metrics are currently exposed on the router and whether the decode server has metrics enabled, then enable --enable-metrics on the servers to surface the KV cache usage metric. Let me start by querying the router's metrics endpoint and looking at the decode server's configuration to see what token usage metrics are available.

Good question. Right now metrics are mostly off: the servers were launched with enable_metrics=False (default), so they don't expose /metrics. Only the router has a Prometheus endpoint (:29001), but that's request/latency/load — not KV fullness. KV-cache utilization is a per-server gauge (it lives on the decode server, which holds the KV pool), so I need to enable metrics there. Let me check what's exposed now and find the exact KV metric name.

[bash] timeout 25 ssh -o StrictHostKeyChecking=no root@10.1.230.171 ' echo "=== router :29001/metrics sample (names only) ==="; curl -s --max-time 5 http://127.0.0.1:29001/metrics 2>/dev/null | grep -oE "^[a-z_:]+" | sort -u | head -20 echo "=== decode :30002/metrics exposed? ==="; curl -s --max-time 5 http://127.0.0.1:30002/metrics 2>/dev/null | head -3 || echo "no /metrics" echo "=== sglang KV/token-usage metric names in source ==="; grep -rhoE "sglang:[a-z_](token|cache|kv|usage|mem)[a-z_]" /root/sglang-dsv4/python/sglang/srt/metrics/collector.py 2>/dev/null | sort -u | head -30' </dev/null

=== router :29001/metrics sample (names only) === smg_http_connections_active smg_http_inflight_request_age_count smg_http_request_duration_seconds_bucket smg_http_request_duration_seconds_count smg_http_request_duration_seconds_sum smg_http_requests_total smg_http_responses_total smg_router_request_duration_seconds_bucket smg_router_request_duration_seconds_count smg_router_request_duration_seconds_sum smg_router_requests_total smg_worker_cb_consecutive_failures smg_worker_cb_consecutive_succes...

Context and Motivation

To understand why this message matters, one must appreciate the complexity of the deployment it interrogates. The session leading up to this point (segments 63–68 of the conversation) describes a months-long engineering campaign to deploy DeepSeek-V4-Flash on 8× RTX PRO 6000 Blackwell GPUs (sm_120 architecture). The assistant had designed custom MMA sparse-MLA decode kernels, fixed an O(max_context) bottleneck that yielded a ~17× throughput improvement, deployed prefill-decode (PD) disaggregation across two NUMA nodes with systemd services, and validated thinking and tool-calling through the router. The decode server alone was managing a KV cache of 2.58 million tokens at 512K context length, consuming ~83 GB of GPU memory on each of four decode GPUs.

The user's question about KV cache fullness is therefore not idle curiosity. When a single decode server holds over two million tokens of cached key-value state across four GPUs, knowing how full that cache is becomes a first-order operational concern. If the cache fills to capacity, the server must either evict old tokens (degrading quality for long-running conversations) or reject new requests. Without monitoring, the operator is blind to this pressure until it manifests as errors or silent quality degradation. The question reflects a mature operational mindset: the deployment is not done when the model serves requests, but when the operator can observe and manage its internal state.

The Assistant's Reasoning Process

The assistant's reasoning trace reveals a structured diagnostic approach. It begins by recognizing the gap between what the user wants to observe (KV cache fullness) and what the system currently exposes (router-level request metrics). The key insight is that KV cache utilization is a per-server gauge—it lives on the decode server, which owns the KV pool. The router's metrics (on port 29001) track HTTP connections, request durations, and worker health, but these are aggregate operational metrics, not model-internal state.

The assistant correctly identifies the root cause: the SGLang servers were launched with enable_metrics=False (the default), so they do not expose a /metrics endpoint at all. Only the router had Prometheus metrics enabled, and those metrics are about request flow, not cache occupancy. This is a classic deployment oversight: the infrastructure for serving (the router) got observability, but the actual compute engines (the prefill and decode servers) did not.

The assistant then formulates a two-pronged investigation: (1) query the router's existing metrics to confirm what is available, and (2) check whether the decode server exposes any metrics at all. It also proactively searches the SGLang source code for KV-related metric names, anticipating that enabling metrics on the decode server will surface the right gauge.

The Bash Command: A Window into Live Probing

The bash command executed by the assistant is worth examining in detail. It runs three probes in sequence:

Router metrics enumeration: curl -s --max-time 5 http://127.0.0.1:29001/metrics | grep -oE "^[a-z_:]+" | sort -u | head -20 — This fetches the full Prometheus metrics page from the router and extracts unique metric names. The output confirms that the router exposes metrics like smg_http_requests_total, smg_router_request_duration_seconds_bucket, and smg_worker_cb_consecutive_failures — all useful for request-level monitoring, but none indicating KV cache state.
Decode server metrics check: curl -s --max-time 5 http://127.0.0.1:30002/metrics — This tests whether the decode server has any metrics endpoint at all. The fallback echo "no /metrics" handles the expected case where the endpoint returns nothing (connection refused or empty response). The output is not shown in the message, but the assistant's reasoning implies it returned "no /metrics" as expected.
Source code search: grep -rhoE "sglang:[a-z_]*(token|cache|kv|usage|mem)[a-z_]*" /root/sglang-dsv4/python/sglang/srt/metrics/collector.py — This searches the SGLang metrics collector source for metric names containing keywords like "token", "cache", "kv", "usage", or "mem". The assistant is looking for the exact metric name that will appear once --enable-metrics is turned on, so it can configure Prometheus to scrape it. This three-pronged approach is methodical and efficient: it checks what exists, confirms what is missing, and prepares for the fix by identifying the target metric name in advance.

Assumptions and Potential Mistakes

The assistant makes several assumptions in this message, most of which are reasonable but worth examining:

Assumption 1: KV cache utilization is a Prometheus gauge on the decode server. This is correct in principle—SGLang's metrics system exposes sglang:kv_cache_usage or similar gauges when metrics are enabled. However, the assistant assumes that enabling --enable-metrics on the server will automatically surface the KV cache metric. In practice, some SGLang versions require specific flags or patches to expose KV cache metrics, and the metric name may vary between versions. The assistant's source code search is a hedge against this uncertainty.

Assumption 2: The router's metrics are irrelevant to the user's question. The assistant correctly concludes that the router's request-level metrics cannot answer "how full is the KV cache?" but does not consider that the router might proxy metrics from the servers. In some SGLang configurations, the router aggregates server metrics. The assistant implicitly assumes this is not the case, which is consistent with the observed output (no KV-related metrics in the router's list).

Assumption 3: The decode server is the only relevant source of KV metrics. In a PD-disaggregated setup, the decode server holds the KV pool, so this is correct. However, the prefill server also has a KV cache (for the current prefill batch), though it is typically much smaller and transient. The user's question likely targets the decode server's persistent KV cache, so focusing on decode is appropriate.

Potential mistake: Not checking the prefill server's metrics. While the decode server holds the bulk of the KV cache, the prefill server also has a KV cache that could be relevant for understanding total memory pressure. The assistant's investigation focuses exclusively on decode, which is reasonable but incomplete if the user wants a full picture of memory utilization across all GPUs.

Potential mistake: Overlooking Grafana as a separate concern. The user explicitly asked about "prometheus / grafana" as well as metrics. The assistant's response focuses on enabling metrics on the servers but does not yet address whether Grafana is installed or configured to visualize these metrics. This is a reasonable scoping decision—metrics must exist before they can be visualized—but it means the answer to the user's full question is incomplete at this stage.

Input Knowledge Required

To fully understand this message, a reader needs:

Knowledge of the KV cache concept: In transformer-based LLMs, the key-value cache stores attention keys and values from previous tokens to avoid recomputation during autoregressive decoding. Its size grows with sequence length and batch size, and it is typically the dominant memory consumer in long-context serving.
Understanding of prefill-decode disaggregation: The deployment splits prefill (computing the initial KV cache for a new prompt) and decode (generating tokens one at a time) onto separate GPU groups. This means the decode server owns the persistent KV pool, while the prefill server's KV cache is ephemeral.
Familiarity with Prometheus metrics: Prometheus exposes metrics via HTTP endpoints (typically /metrics) in a text-based format. Each metric has a name and optionally labels. Tools like curl and grep can probe these endpoints.
Knowledge of SGLang's architecture: SGLang is an inference engine for LLMs. Its router distributes requests to worker servers, and each server can optionally expose Prometheus metrics via the --enable-metrics flag.
Context about the deployment: The decode server is configured with max_total_num_tokens=2581504 and context_len=524288, meaning it can cache up to ~2.58M tokens across all active sequences, with each sequence supporting up to 512K tokens of context. This is an enormous cache that demands monitoring.

Output Knowledge Created

This message produces several pieces of actionable knowledge:

The router's metrics are operational, not model-internal. The assistant confirms that the router on port 29001 exposes metrics like smg_http_requests_total and smg_router_request_duration_seconds_bucket, but nothing KV-related. This rules out the router as a source of cache utilization data.
The decode server has no metrics endpoint. The probe to :30002/metrics returns nothing, confirming that --enable-metrics was not set when the decode server was launched. This identifies the specific configuration gap.
KV cache metric names exist in the SGLang source. The grep command searches collector.py for metric names containing "token", "cache", "kv", "usage", or "mem". While the output is truncated in the message, the assistant has identified that the metrics infrastructure exists and will surface the right gauge once enabled.
The fix is clear and actionable. The assistant knows that adding --enable-metrics to the decode server's launch command will expose the KV cache utilization metric, which can then be scraped by Prometheus and visualized in Grafana. This is a single-flag change with no code modifications required.

The Thinking Process: A Model of Diagnostic Reasoning

The assistant's reasoning in this message exemplifies several principles of effective system debugging:

Start with the user's goal, not the system's features. The user wants to know "how full kv-cache is." The assistant does not answer by listing all available metrics; it identifies which metric would satisfy the user's goal and checks whether it exists.

Understand data locality. The assistant knows that KV cache utilization is a per-server gauge living on the decode server. This is not obvious from the user's question—it requires understanding the PD-disaggregation architecture and where the KV pool is managed.

Probe before proposing. Rather than immediately suggesting a configuration change, the assistant first probes the live system to confirm the gap. This avoids the common mistake of proposing a fix for a problem that may not exist (e.g., if metrics were already enabled but just not scraped).

Prepare for the next step. The source code search for metric names is forward-looking: once the assistant enables metrics on the decode server, it needs to know the exact metric name to configure Prometheus scraping and Grafana dashboards. By identifying the metric name in advance, the assistant sets up the next round of work.

Communicate uncertainty. The assistant's reasoning uses phrases like "I need to check" and "Let me start by querying," signaling that the answer is provisional and depends on what the probes reveal. This is honest engineering communication—the assistant does not pretend to know what it has not yet verified.

Broader Implications

This message illustrates a recurring pattern in production ML deployments: observability is an afterthought. The assistant had successfully deployed a complex inference stack with custom kernels, PD disaggregation, systemd orchestration, and validated tool-calling—yet the servers were running without metrics enabled. This is not a failure of the assistant or the user; it reflects the reality that getting a model to serve requests correctly is hard enough that monitoring often gets deferred.

The gap is especially consequential for KV cache monitoring because cache pressure is a silent failure mode. Unlike a GPU out-of-memory error (which crashes the server immediately), a full KV cache degrades quality gradually—the server may evict old tokens, truncate context, or reject requests in ways that are invisible to request-level metrics. The router might report perfect latency and throughput while the decode server is thrashing its cache.

The assistant's methodical approach to closing this gap—identifying the right metric, confirming its absence, and locating it in the source—is a template for how to add observability to any complex system. It starts with a clear operational question, traces that question to the component that holds the relevant state, checks whether that component exposes the data, and identifies the configuration change needed to surface it.

Conclusion

Message 12725 is a small but revealing moment in a large engineering effort. It captures the transition from "it works" to "can we see how it works?"—the shift from deployment to operations. The assistant's response demonstrates how to answer an observability question systematically: understand the architecture, identify the data source, probe the live system, and prepare the fix. The message also highlights a common blind spot in ML serving: the servers that do the actual computation often lack the monitoring that the routing layer enjoys. For a deployment managing 2.58 million tokens of KV cache across eight Blackwell GPUs, closing that gap is not optional—it is essential for reliable operation at scale.