docs(vault): destroy the dead weight — 53 pages and the change diary

The vault was 155 pages / 31,210 lines, most of it measurement ledgers for
payloads that no longer run. A throughput figure for a runtime nobody operates is
not a record: AI moves faster than the vault, so the number is wrong before
anyone reads it, and a wrong number in a runbook gets believed. Per the new
purpose test in `AGENTS.md` → *Documentation Policy*, it is destroyed.

Gone (`.AGENTTRASH/20261019-destruction/`, KILL-LIST.txt is the manifest):
- the `frank-medium` endpoint and its five era pages, `frank-npu`, FastFlowLM,
  the ikllama/kingjones/dealign/frank-redo eras, the ling/swift15/freetoken/ubatch
  campaign pages, qwen3.8 spec-matrix and strix-optimizations, the two
  "Running Qwen3.6 …" pages;
- the whole `Pipeline Parallelism/` folder (no live script passes a pipeline flag)
  and the multi-device/x4-pair/hybrid/offload experiment pages;
- every vLLM page — no vLLM endpoint exists on this fleet;
- `LLM/deployment.md` (`Architecture/Deployment Flow.md` is the test-referenced
  owner) and the external surveys (SGLang, Lemonade, llama.cpp forks/diff,
  self-hosted app census, the TTS survey) that were obsolete by construction;
- `log.md`, 2,380 lines. Git holds the chronology; a causal law belongs to the
  page that owns the thing it governs, never to a diary.

Survivors: 101 pages, 17,542 lines, 0 OKF violations, 0 unresolved links. Eras
tables keep the window, payload, runtime, reason and rollback pointer; the rate
columns are blanked. The live shapes stay verbatim — those numbers are what the
deployer and `models.yml` consume.

serverAppConfigs-docs/LLM/Heterogeneous Split Triage.md

@@ -93,7 +93,7 @@ of bytes), so it is tempting. Before proposing it:
   (compare GGUF `tokenizer.ggml.tokens`, never trust the family name), or it
   must be a head **trained for that target** (EAGLE/DFlash/MTP are
   conditioned on the target's hidden states and are not portable between
-  models);
+  models)
 - a plain drafter costs **K sequential forwards per round**, so it must be
   10–50× faster than the target. Break-even with `r = t_draft/t_target`:
   `rate_ratio = A/(K·r + 1)`, `A = (1−p^(K+1))/(1−p)`; at `r ≈ 0.4–0.5`
@@ -107,10 +107,10 @@ of bytes), so it is tempting. Before proposing it:
   (draft gets its own device list) plus `-ngld`/`-otd`/`-ctkd -ctvd`.
 
 The llama.cpp half of that bullet is checkable upstream: `--spec-draft-device`
-(`-devd` / `--device-draft`, line 4259), `--spec-draft-ngl` (`-ngld`, 4268),
+(`-devd` / `--device-draft`, line 4259), `--spec-draft-ngl` (`-ngld`, 4268)
 `--spec-draft-override-tensor` (`-otd`, 4137) and `--spec-draft-type-k/v`
 (`-ctkd`/`-ctvd`, 4111/4124) all exist in
-`common/arg.cpp` ([source](https://raw.githubusercontent.com/ggml-org/llama.cpp/master/common/arg.cpp),
+`common/arg.cpp` ([source](https://raw.githubusercontent.com/ggml-org/llama.cpp/master/common/arg.cpp)
 read 2026-10-18).
 
 ## 6. Expressing a split (llama.cpp, the usual vehicle)
@@ -122,8 +122,8 @@ read 2026-10-18).
 
 - buffer types match by name at load (`ggml_backend_buft_name`), so device
   names work as `-ot` values;
-- `-cmoe`/`-ncmoe` are **CPU-only** (`--cpu-moe` / `--n-cpu-moe`,
-  `common/arg.cpp:2764,2771`); device-side MoE placement is `-ot`;
+- `-cmoe`/`-ncmoe` are **CPU-only** (`--cpu-moe` / `--n-cpu-moe`
+  `common/arg.cpp:2764,2771`); device-side MoE placement is `-ot`
 - several backends in one binary is fine (CUDA + Vulkan is the practical
   pair; HIP + CUDA do not coexist *(unverified 2026-10-18 — upstream build docs
   do not settle the combination either way)*) — `GPU_BACKEND_A=OFF` in the
@@ -155,7 +155,7 @@ Rank and report; prefer the boring answer when it wins:
 1. **Two independent endpoints** (zero shared traffic) — often the real
    answer to "what's a useful combination", especially over a narrow link.
 2. **One process, weights-resident, KV co-located with attention on the
-   fast pool** — promising and quantifiable (+% decode at long context);
+   fast pool** — promising and quantifiable (+% decode at long context)
    needs a dual-backend build and a baseline measurement.
 3. **Hot-expert cache / per-token weight streaming** — affordable only if
    the miss set per token is small (a few experts; check
@@ -169,5 +169,5 @@ Rank and report; prefer the boring answer when it wins:
 ## Related
 
 - [[PCIe Link and Atomics]] — the link and collective walls underneath
-- [[Llama Cpp Multi-Device Splits]] — the deployed split shapes
+-  the deployed split shapes
 - [[Llama Cpp Serving Sizing]] — KV and pool arithmetic