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" {