frank-medium: serve the whole 262144-token window, and repoint every signal to llama.cpp

Context: 262144 was measured before it was deployed -- 24.72 GB on GPU 0 and
29.14 GB on GPU 1 of 34.21 GB each, ~5 GB of headroom on the busier card, and a
140,069-token prompt prefilled through the single slot in 134 s to prove the
window is real. The 114688 wall was the vLLM predecessor's arithmetic, not this
runtime's.

Throughput, measured through the service: 1.0-1.4k tok/s prefill from 4k to
140k, 43.8 tok/s decode on a 512-token generation, 41-71 % DFlash2 acceptance,
and the prefix cache re-prefilling 4 of 998 tokens on a repeated prompt.

The signals move with the runtime, so the fleet's rules and dashboard are
re-cut rather than renamed: vllm: series do not exist any more, and two of the
old rules watched things llama.cpp does not export at all (KV-pool fill, p95
latency). Those are retired with the reason written down, and LlmSpecDecodeOff
takes one of the slots -- a lost drafter is the failure no client can see. The
observability seam now fails if any rule names a series the endpoint does not
export, which is what would have caught the stale vllm: expressions.

omp models.yml (outside the repo) is updated in the same pass: contextWindow
262144, and the full reasoning-effort map, because Qwen3.8's template answers
500 on 'high' -- verified against the live endpoint, low/medium/xhigh answer
200. Subagents run at high.

shared/grafana/provisioning/dashboards/llm-fleet.json

@@ -1,7 +1,7 @@
 {
   "uid": "llm-fleet",
-  "title": "LLM fleet — capacity and reuse",
-  "description": "The signals that explain frank-medium under load, from the 2026-09-16 capacity work. The panels read the endpoint's own counters: KV fill, queue depth, prefix-cache reuse, and request latency. frank-small (llama.cpp on Dankclaw's RTX 3090) exports throughput counters and appears in the same endpoint picker; frank-large runs the halogen engine, which publishes /health, /cache and /v1/* and no metrics endpoint at all, so it cannot appear here. See serverAppConfigs-docs/LLM/frank-medium-llamacpp-dflash2.md for the numbers these panels are read against.",
+  "title": "LLM fleet \u2014 capacity and reuse",
+  "description": "The signals that explain the LLM endpoints under load, from the 2026-09-16 capacity work. The panels read llama.cpp's own counters: speculative acceptance, queue depth, prefix-cache reuse, decode step rate and throughput. frank-medium (Dankserver, both R9700s, layer split) and frank-small (Dankclaw, RTX 3090) both export them; frank-large runs the halogen engine, which publishes /health, /cache and /v1/* and no metrics endpoint at all, so it cannot appear here. See serverAppConfigs-docs/LLM/frank-medium-llamacpp-dflash2.md for the numbers these panels are read against.",
   "tags": [
     "llm",
     "frank-medium",
@@ -42,7 +42,7 @@
     {
       "id": 1,
       "type": "timeseries",
-      "title": "KV pool fill (vllm:kv_cache_usage_perc)",
+      "title": "Speculative decoding (DFlash2 acceptance)",
       "description": "How full each card's KV pool is. One card holds ~122,636 tokens; sustained high values with a cold prefix cache is the thrash this fleet measured.",
       "gridPos": {
         "h": 8,
@@ -69,8 +69,13 @@
       "targets": [
         {
           "refId": "A",
-          "expr": "vllm:kv_cache_usage_perc{job=~\"$job\"} * 100",
-          "legendFormat": "{{job}} engine {{engine}}"
+          "expr": "rate(llamacpp:spec_decode_num_accepted_tokens_total{job=~\"$job\"}[5m]) / clamp_min(rate(llamacpp:spec_decode_num_drafts_total{job=~\"$job\"}[5m]), 0.001)",
+          "legendFormat": "{{job}} accepted per draft step"
+        },
+        {
+          "refId": "B",
+          "expr": "rate(llamacpp:spec_decode_num_draft_tokens_total{job=~\"$job\"}[5m])",
+          "legendFormat": "{{job}} draft tokens/s"
         }
       ]
     },
@@ -103,13 +108,13 @@
       "targets": [
         {
           "refId": "A",
-          "expr": "vllm:num_requests_waiting{job=~\"$job\"}",
-          "legendFormat": "{{job}} waiting"
+          "expr": "llamacpp:requests_deferred{job=~\"$job\"}",
+          "legendFormat": "{{job}} deferred"
         },
         {
           "refId": "B",
-          "expr": "vllm:num_requests_running{job=~\"$job\"}",
-          "legendFormat": "{{job}} running"
+          "expr": "llamacpp:requests_processing{job=~\"$job\"}",
+          "legendFormat": "{{job}} processing"
         }
       ]
     },
@@ -143,7 +148,7 @@
       "targets": [
         {
           "refId": "A",
-          "expr": "rate(vllm:prefix_cache_hits_total{job=~\"$job\"}[5m]) / clamp_min(rate(vllm:prefix_cache_queries_total{job=~\"$job\"}[5m]), 1)",
+          "expr": "rate(llamacpp:prompt_tokens_cached_total{job=~\"$job\"}[5m]) / clamp_min(rate(llamacpp:prompt_tokens_total{job=~\"$job\"}[5m]), 1)",
           "legendFormat": "{{job}} reuse"
         }
       ]
@@ -151,8 +156,8 @@
     {
       "id": 4,
       "type": "timeseries",
-      "title": "End-to-end request latency p95",
-      "description": "Wall-clock per request, prefill included — the number an agent-loop turn feels.",
+      "title": "Decode steps and batch size",
+      "description": "Wall-clock per request, prefill included \u2014 the number an agent-loop turn feels.",
       "gridPos": {
         "h": 8,
         "w": 12,
@@ -176,8 +181,13 @@
       "targets": [
         {
           "refId": "A",
-          "expr": "histogram_quantile(0.95, sum by (le, job) (rate(vllm:e2e_request_latency_seconds_bucket{job=~\"$job\"}[5m])))",
-          "legendFormat": "{{job}} p95"
+          "expr": "rate(llamacpp:n_decode_total{job=~\"$job\"}[5m])",
+          "legendFormat": "{{job}} decode steps/s"
+        },
+        {
+          "refId": "B",
+          "expr": "llamacpp:n_busy_slots_per_decode{job=~\"$job\"}",
+          "legendFormat": "{{job}} sequences per decode step"
         }
       ]
     },
@@ -207,16 +217,6 @@
         "overrides": []
       },
       "targets": [
-        {
-          "refId": "A",
-          "expr": "rate(vllm:generation_tokens_total{job=~\"$job\"}[5m])",
-          "legendFormat": "{{job}} generated tok/s"
-        },
-        {
-          "refId": "B",
-          "expr": "rate(vllm:prompt_tokens_total{job=~\"$job\"}[5m])",
-          "legendFormat": "{{job}} prompt tok/s"
-        },
         {
           "refId": "C",
           "expr": "llamacpp:predicted_tokens_seconds{job=~\"$job\"}",
@@ -233,7 +233,7 @@
       "id": 6,
       "type": "timeseries",
       "title": "Alerts firing (ALERTS)",
-      "description": "Prometheus rules for the LLM fleet (shared/prometheus/rules/llm-fleet.yml): pool saturation, queueing, prefix-cache collapse, endpoint down, p95 latency. A firing alert appears here, on Prometheus' /alerts page, and nowhere else — this stack has no Alertmanager, so nothing is notified.",
+      "description": "Prometheus rules for the LLM fleet (shared/prometheus/rules/llm-fleet.yml): pool saturation, queueing, prefix-cache collapse, endpoint down, p95 latency. A firing alert appears here, on Prometheus' /alerts page, and nowhere else \u2014 this stack has no Alertmanager, so nothing is notified.",
       "gridPos": {
         "h": 8,
         "w": 12,