feat(fv-ml1): rebalance GPUs 0/1 by usage; contexts to published maximums; gen-small default output cap

Layout: GPU 0 = cyberprev (KV 16 -> 15 GiB, 1.29x), erp-seat (moved from
GPU 1, KV 8.49 -> 5.5 GiB, 1.32x), voices (ctx 8,192 -> 40,960, its native
maximum, KV 6 GiB, 1.07x), coder (moved from GPU 1, ctx 16,384 -> 32,768,
the card maximum, 2.35x), parakeet-nemo. GPU 1 = gen-small (moved from
GPU 0, 5.5 GiB, 1.76x), meromero-rp (22 GiB, 1.32x), intern-decision.

Sizing rule: KV >= 1.25x max context rounded up to 0.5 GiB, checked against
7 days of LiteLLM traffic and the engines' own KV-usage logs. Each card keeps
spare memory for its bursty tenant (parakeet's cap on GPU 0, intern-decision's
32k call on GPU 1). YaRN extensions are not enabled: they do not fit, and the
Qwen cards warn static YaRN degrades short prompts.

gen-small gets --override-generation-config max_new_tokens 32768. It applies
only to requests that omit max_tokens (vLLM get_max_tokens) and answers 9
runaway ~260k-token replies in 7 days; the longest normal reply was 6,357.

Applied with playbooks/fv-ml1-rebalance-20261005.yaml (boot-check guard and
health/KV verification per seat); all nine seats healthy, gateway smoke
tests pass.

stacks/erp-seat/README.md

@@ -1,4 +1,4 @@
-# erp-seat — ERP-tune seat on ana-ml2 (GPU1, `:8021`)
+# erp-seat — ERP-tune seat on fv-ml1 (ex ana-ml2; GPU0 since 2026-10-05, `:8021`)
 
 Serves the latest gated ERP LoRA merge as an **NVFP4A16** (weight-only) compressed-tensors
 checkpoint so the GX10 is free to train the next run.
@@ -38,7 +38,7 @@ flip plus `docker compose up -d`.*
   json_schema` (guided decoding) 18/18 on this seat** — that is the deterministic path for a
   forced call; `tool_choice: auto` works normally. A parser plugin that re-enables guided JSON
   would also need JSON extraction in the engine-parser path; not attempted.
-- **GPU1 is shared** — check real usage (`nvidia-smi --query-compute-apps=pid,used_memory`) before
+- **GPU0 is shared** (since 2026-10-05: cyberprev, voices, coder, parakeet-nemo; KV pinned 5.5 GiB = 346,436 tokens, 1.32×) — check real usage (`nvidia-smi --query-compute-apps=pid,used_memory`) before
   raising `ERP_GPU_MEM_UTIL`; the flag sizes KV, not CUDA context.
 - **Rollback / next run:** point `ERP_MODEL` + `ERP_SERVED_NAME` at the next quant dir, keep the
   previous on disk. Deploy with `scripts/deploy-stack.sh ana-ml2 erp-seat`.