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.

scripts/frank-medium.env

@@ -50,11 +50,19 @@
 # in the attention layers only, plus ~0.16 GiB per sequence of f32 DeltaNet
 # state at any depth.
 #
-# SIZING (per card, 31.86 GiB): weights split ~14.7 GiB; the drafter rides one
-# card; KV at CTX_SIZE 131,072 is 8.00 GiB total (~4 GiB per card, since the 16
-# attention layers are split with everything else); the loader's compute buffers
-# complete the ledger.  Read the loader's own "KV self size" / "compute buffer
-# size" lines rather than trusting this arithmetic.
+# SIZING, measured (rocm-smi, 2026-09-16).  Weights split across the two cards;
+# the drafter rides the second.  The loader's compute buffers are part of what
+# is seen below -- they are allocated at load, not per request:
+#
+#   CTX_SIZE   GPU 0        GPU 1        headroom on the busier card
+#   131072     19.89 GB     24.30 GB     9.9 GB / 31 %
+#   262144     24.72 GB     29.14 GB     5.07 GB / 15 %   <- deployed
+#
+# 262144 is the window the model ships (n_ctx_train) and it fits with room, so
+# the fleet gets the whole thing; the arithmetic that used to cap this endpoint
+# at 114688 was the vLLM predecessor's, not this runtime's.  If a future change
+# adds VRAM (a longer drafter, quantized KV, more slots) re-read the numbers
+# rather than assuming, and prefer CTX_SIZE over --gpu-layers.
 #
 # WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE (HIP reads
 # those bare vars; 2026-08-13 incident).  llama.cpp takes --host/--port/--temp
@@ -74,9 +82,9 @@ DRAFT_MODEL_FILE=Qwen3.8-27B-DFlash2-Q8_0.gguf
 ALIAS=frank-medium
 BIND_ADDR=0.0.0.0
 LISTEN_PORT=7770
-
-# Placement.  HIP_VISIBLE_DEVICES hides the iGPU; DEVICES is llama.cpp's own
-# list, which is the pair the split runs across.
+# Device placement.  HIP_VISIBLE_DEVICES hides the Raphael iGPU (HIP device 2)
+# so nothing can drift onto shared system RAM; DEVICES is what llama.cpp splits
+# across, and neither of the two cards has an XGMI link to the other.
 HIP_VISIBLE_DEVICES=0,1
 DEVICES=ROCm0,ROCm1
 SPLIT_MODE=layer
@@ -88,11 +96,11 @@ NGL=999
 FIT=off
 FLASH_ATTN=on
 
-# One slot at the model's documented-configured window.  --parallel divides
-# --ctx-size between slots; it does not multiply.  The target model ships
-# n_ctx_train 262144 -- raising CTX_SIZE is a measurement, not a guess (watch
-# the per-card ledger), and this service was deployed at 131072 first.
-CTX_SIZE=131072
+# One slot at the model's full configured window (n_ctx_train 262144), which was
+# measured to fit before it was deployed: see SIZING above.  --parallel DIVIDES
+# --ctx-size between slots, so one slot carries the whole window and a second
+# concurrent request queues behind it.
+CTX_SIZE=262144
 PARALLEL=1
 
 BATCH_SIZE=2048