fix(monitoring): llama.cpp reuse ratio, dashboard re-cut for both namespaces, measured prefix-cache granularity

- shared/prometheus/rules/llm-fleet.yml: the llamacpp reuse recording rule
  divided cached by prompt, but llama.cpp's prompt_tokens_total EXCLUDES
  cached tokens (its own HELP line), so the ratio was unbounded — measured
  77 on a fully-warm frank-large, meaning LlmPrefixCacheCollapse could never
  fire on that side. Now cached / (cached + prompt), with the traffic floor
  counting both terms too; the header documents the unit trap. Also fixed the
  LlmEndpointDown description: frank-large's CIRU llama.cpp build DOES export
  the llamacpp:* family (verified live), so the alert covers all three
  endpoints.
- shared/grafana/provisioning/dashboards/llm-fleet.json: re-cut for two
  namespaces. Every panel reads both (the endpoint picker selects); the
  KV-pool-fill panel retired 2026-09-16 returned with
  vllm:kv_cache_usage_perc; the latency p95 panel returned with the vLLM
  histograms (TTFT/ITL/e2e); the alerts panel is scoped to the fleet's
  Llm.* rules; the stale halogen-era description is gone. All 21 panel
  expressions validated against the live endpoints.
- runbook + log + Observability: prefix caching measured, not inferred —
  reuse happens at 1,648-token block granularity (attention block size padded
  to the mamba page size); an identical 13,021-token prompt went 2.30 s cold
  to 0.68 s warm with 9,888 tokens (76 %) reused, while a 2,521-token prompt
  registered zero hits. Rule counts corrected to 6 alerts + 3 recording rules.

./tests/run.sh: 325 pass.

serverAppConfigs-docs/LLM/frank-medium-radiance-mxfp4.md

@@ -68,6 +68,27 @@ against the host's 62 GiB and stayed flat through a 4-way burst at 512-token
 max; host-wide swap 3.3 GiB, unchanged. The 2026-09-16 swap storm was the
 two-replica vision shape, not this one.
 
+## Prefix caching — measured granularity (2026-09-20)
+
+`--enable-prefix-caching` is on, and radiance says so at boot: *"Prefix caching
+in Mamba cache 'align' mode is currently enabled. Its support for Mamba layers
+is experimental."* The attention block size is **1,648 tokens** (boot log:
+"Setting attention block size to 1648 tokens …"), padded to the mamba/GDN page
+size, and reuse happens at that granularity — not per token.
+
+Measured on the deployed endpoint, not inferred:
+
+| Probe | Cold | Warm | `vllm:prefix_cache_hits_total` delta |
+|---|---|---|---|
+| identical 13,021-token prompt | 2.30 s | 0.68 s (3.4×) | +9,888 of 13,021 queried (76 %) |
+| identical 2,521-token prompt | 0.51 s | 0.51 s | +0 (under two blocks) |
+
+Consequence: a shared prefix shorter than about two blocks (≈3.3k tokens)
+re-prefills in full, so the benefit starts at ~3.3k tokens of shared prefix.
+The API returns `prompt_tokens_details: null` in this build, so the
+`vllm:prefix_cache_hits_total` / `vllm:prefix_cache_queries_total` counters
+(both token-counted) are the only instrument — see [[Observability]].
+
 ## Battery results — why this shape won (2026-09-20)
 
 `scripts/bench-conversation.py`, 10 real-workload prompts, `max_tokens=512`,