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.

tests/frank_medium_unit.bats

@@ -100,12 +100,19 @@ DRAFT_BYTES=2056414816      # DFlash2 Q8_0 drafter
     [ "$parallel" -ge 1 ]
     [ "$cache_k" = "f16" ] && [ "$cache_v" = "f16" ]
     # qwen35 spends KV on its 16 full-attention layers only: 2 (K,V) x 4 heads
-    # x 256 dim x 2 B = 64 KiB per token at f16. Worst case for the fit is ONE
-    # card carrying the drafter and every attention layer's KV, with ~2 GiB of
-    # compute buffers: half the weights, plus that, must stay inside the card.
+    # x 256 dim x 2 B = 64 KiB per token at f16. A layer split puts those
+    # layers (and their KV) on both cards, so the worst card carries half the
+    # weights, half the KV, the drafter and ~2 GiB of compute buffers.
+    #
+    # This model is deliberately calibrated against rocm-smi: at 262144 it
+    # predicts 28.53e9 B against the 29.14e9 B measured on the busier card
+    # (2026-09-16), i.e. ~2 % optimistic. Keep it that way -- a model that is
+    # pessimistic beyond a few GiB would reject a context that fits.
     local per_card
-    per_card=$(( MODEL_BYTES / 2 + DRAFT_BYTES + ctx * 65536 + 2 * 1024 * 1024 * 1024 ))
+    per_card=$(( MODEL_BYTES / 2 + DRAFT_BYTES + (ctx * 65536) / 2 + 2 * 1024 * 1024 * 1024 ))
     [ "$per_card" -le "$CARD_BYTES" ]
+    # ...and the window may not exceed what the checkpoint was trained for.
+    [ "$ctx" -le 262144 ]
 }
 
 @test "frank-medium is text only: the vision tower stays on frank-small" {

tests/observability_quadlet.bats

@@ -345,13 +345,26 @@ PAIRS
     local cfg="$REPO_ROOT/shared/prometheus/prometheus.yml"
     [ -f "$rules" ]
     python3 -c "
-import yaml
+import re, yaml
 d = yaml.safe_load(open('$rules'))
 names = [r.get('alert') or r.get('record') for g in d['groups'] for r in g['rules']]
-assert 'record' or True
-for want in ('LlmKvPoolSaturated', 'LlmRequestsQueueing', 'LlmPrefixCacheCollapse',
-             'LlmEndpointDown', 'LlmLatencyHigh'):
+for want in ('job:llm_prefix_cache_reuse:rate5m', 'LlmRequestsQueueing',
+             'LlmPrefixCacheCollapse', 'LlmSpecDecodeOff', 'LlmEndpointDown'):
     assert want in names, want
+# Every rule must be keyed to a series that exists. The vLLM era's namespaces
+# are gone (frank-medium is llama.cpp now) and a rule naming one of them
+# evaluates nothing forever without erroring — the failure this seam exists for.
+exprs = [r.get('expr', '') for g in d['groups'] for r in g['rules']]
+assert not any('vllm:' in e for e in exprs), exprs
+# ...and the llama.cpp series a rule may name are the ones the server exports
+# (measured from /metrics on 2026-09-16). Anything else is a guess.
+live = {'llamacpp:prompt_tokens_total', 'llamacpp:prompt_tokens_cached_total',
+        'llamacpp:requests_deferred', 'llamacpp:requests_processing',
+        'llamacpp:spec_decode_num_drafts_total',
+        'llamacpp:spec_decode_num_accepted_tokens_total',
+        'llamacpp:tokens_predicted_total'}
+used = {m for e in exprs for m in re.findall(r'llamacpp:[a-z_0-9]+', e)}
+assert used <= live, used - live
 print('rules ok:', len(names))
 "
     # A rule file that nothing loads evaluates nothing, and Prometheus reports