diff --git a/stacks/flash-next-seat/.env.example b/stacks/flash-next-seat/.env.example
new file mode 100644
index 0000000..e850df4
--- /dev/null
+++ b/stacks/flash-next-seat/.env.example
@@ -0,0 +1,87 @@
+# flash-next-seat — Qwen3.8-Flash-Next (abliterated) on fv-ml1 GPU 2, :8022.
+# Copy to .env on the host at /opt/docker/compose/flash-next-seat/.env.
+#
+# This is the INITIAL configuration, stood up 2026-09-13. Values marked FIRST-BOOT
+# are deliberately conservative and expected to be revised once the seat has
+# reported its own memory budget and been bisected for depth. Do not treat them as
+# measured — they are not yet.
+
+# ── Image ───────────────────────────────────────────────────────────────────
+# ⚠ MUST contain vLLM #54371 (UVA PLE-offload), merged 2026-09-09T14:32Z.
+# Verified by ancestry rather than version string: this commit is +150 / behind_by=0
+# from merge commit 3116c5d06bfe76501b3dd6b5434bfc7f3274f5e7. v0.29.0 does NOT
+# contain it (cut ~6h before the merge) and neither does any nightly-<sha> tag
+# dated 2026-09-09 or earlier — the nightly build runs ~06:16 UTC.
+FN_IMAGE=vllm/vllm-openai:nightly-eed1f3d0c6043bd494424a22443ee198dd56f657
+API_KEY=replace-me
+
+# ── Placement ───────────────────────────────────────────────────────────────
+# GPU 2 was completely idle (2 MiB) before this seat; GPU 3 still is. Every other
+# compose GPU pin on fv-ml1 is 0 or 1, so this seat displaced nothing.
+FN_GPU_ID=2
+FN_PORT=8022
+FN_CONTAINER_NAME=vllm-flash-next
+
+# ── Model ───────────────────────────────────────────────────────────────────
+# dealignai/Qwen3.8-Flash-Next-ABLITERATED-NVFP4 @ be794b990578ef3031eccf9f28e675a289a09ee9
+FN_MODEL=/tank/aimodels/qwen38-flash-next-abliterated-nvfp4
+FN_QUANT=modelopt_fp4
+FN_SERVED_NAME=qwen3.8-flash-next-uncensored
+FN_SERVED_NAME_THINK=qwen3.8-flash-next-uncensored-thinking
+
+# ── The offload ─────────────────────────────────────────────────────────────
+# The 51B n-gram table (47.7 GiB FP8) lives in pinned host RAM; the GPU reads rows
+# over CUDA UVA. Without this the checkpoint needs ~126 GiB of VRAM and will not
+# start on one 96 GiB card. fv-ml1 has 566 GB RAM / ~388 GB available, so the
+# host side is not a constraint here — unlike every DGX-Spark report upstream,
+# where host and device share one unified pool and "offload" frees nothing.
+FN_ENGRAM_CONFIG={"cpu_offload": true}
+
+# ── Memory and context ──────────────────────────────────────────────────────
+# FIRST-BOOT. Card is dedicated, so 0.90 (the vLLM recipe's own figure for offload
+# configs) is not competing with a neighbour. Expected resident weights ~78 GiB of
+# 95.6, leaving ~8-9 GiB for KV + activation inside the 0.90 budget.
+# ⚠ NEXT STEP after first boot: read vLLM's budget line and replace this with an
+# explicit --kv-cache-memory in BYTES. A ratio sizes the KV calculation but does
+# not bound peak activation — that is what bit stacks/mog-sec twice on 2026-09-10.
+FN_GPU_MEM_UTIL=0.90
+
+# FIRST-BOOT. 128K, against a 262,144 native ceiling. Raising this is gated on a
+# depth bisect with a NON-REPEATING prompt, because two open upstream issues make
+# depth the risky axis: #54764 (PLE short-conv prefill pads every request in a
+# batch to the batch-max query length) and #54919 (long prefill starving decode
+# for 3-7 minutes). See the compose comment.
+FN_MAX_MODEL_LEN=131072
+FN_MAX_NUM_SEQS=16
+FN_MAX_NUM_BATCHED_TOKENS=8192
+FN_MAMBA_CACHE_MODE=align
+
+# ── Prefix caching ──────────────────────────────────────────────────────────
+# ⚠ THE ROLLBACK LEVER for open #54173 (CUBLAS_STATUS_INTERNAL_ERROR / illegal
+# memory access in the GDN path, WITH prefix caching). Set to the empty string to
+# disable. Leave FN_MAMBA_CACHE_MODE=align either way — Qwen4Exp raises on "all".
+FN_PREFIX_CACHING=--enable-prefix-caching
+
+# ── Vision ──────────────────────────────────────────────────────────────────
+# 4194304 px = 2048x2048 -> ~5,125 image tokens. The checkpoint's own preprocessor
+# declares 16777216 (4096x4096) -> ~16,384 tokens, which is both wasteful and fatal
+# on builds enforcing the image-token count check. Same trap as stacks/mog-sec.
+FN_MM_PROCESSOR_KWARGS={"size": {"longest_edge": 4194304, "shortest_edge": 65536}}
+FN_LIMIT_MM={"image": 4}
+
+# ── Misc ────────────────────────────────────────────────────────────────────
+FN_REASONING_PARSER=qwen3
+FN_REASONING_EFFORT=medium
+FN_TOOL_CALL_PARSER=qwen3_xml
+# Intentionally EMPTY. expandable_segments has corrupted retained tensors on this
+# box before (quant playbook §3.10) and has never been tested against a pinned
+# host allocation handed to UVA.
+FN_ALLOC_CONF=
+
+# ── NOT SET, on purpose ─────────────────────────────────────────────────────
+# --speculative-config  : MTP is off. vLLM's own recipe measured it WORSE at every
+#                         concurrency on 4xH100 (8-36% less throughput, 32-173%
+#                         more latency, ~36% acceptance) and open #55357 reports
+#                         episodic 0% acceptance with repetition collapse.
+# --kv-cache-dtype fp8  : fp8_e4m3 KV on this model's QSA path is an unmerged RFC
+#                         (#54426). Do not copy it over from gen/mog-sec.
diff --git a/stacks/flash-next-seat/README.md b/stacks/flash-next-seat/README.md
new file mode 100644
index 0000000..4dfadd9
--- /dev/null
+++ b/stacks/flash-next-seat/README.md
@@ -0,0 +1,139 @@
+# flash-next-seat — Qwen3.8-Flash-Next (abliterated), fv-ml1 GPU 2, `:8022`
+
+The first seat on the fleet whose weights do not fit its card and run anyway.
+
+`Qwen3.8-Flash-Next` is 176B total — a 125B main model plus a **51B n-gram (PLE)
+lookup table** — activating ~6B parameters per token. The n-gram table is a pure
+embedding lookup with almost no compute per token, so it lives in **pinned host
+RAM** and the GPU reads the rows it needs directly over **CUDA UVA** on a dedicated
+stream with async prefetch.
+
+| | |
+|---|---|
+| Checkpoint | `dealignai/Qwen3.8-Flash-Next-ABLITERATED-NVFP4` @ `be794b99…` (126.0 GiB) |
+| On the card | ~78 GiB of 95.6 GiB — routed experts NVFP4 W4A4, rest at source precision |
+| In host RAM | 47.7 GiB pinned, FP8 E4M3, 10 `model-plefp8-*` shards + per-table scalar scale |
+| Context | 131,072 to start (native ceiling 262,144) — see *Raising context* |
+| Speculative decoding | **none** — see *Why MTP is off* |
+| Gateway wiring | **none yet** — this seat is not in LiteLLM; `gen` is untouched |
+
+## Deploy
+
+```bash
+scripts/deploy-stack.sh fv-ml1 flash-next-seat     # diffs vs live, prompts y/N
+# then on the host, first boot only:
+ssh infra-ops@10.251.50.54 'cd /opt/docker/compose/flash-next-seat && docker compose config >/dev/null && docker compose up -d'
+```
+
+The `.env` lives on the host and is never committed. Copy `.env.example`, set
+`API_KEY`, and read the FIRST-BOOT annotations before changing anything else.
+
+## Architecture, briefly
+
+Four ideas, and three of them shape the serving config:
+
+- **GDN + QSA.** 36 of 48 layers use Gated DeltaNet (linear attention) to compress
+  history; every fourth layer uses Qwen Sparse Attention for long-range retrieval.
+  This is why KV is cheap at depth and why `--mamba-cache-dtype float32` matters.
+- **N-gram embedding.** The 51B lookup table that this seat offloads. Qwen's own
+  framing: capacity with almost no per-token compute.
+- **Gated residual / hyper-connections.** Four residual branches; excluded from
+  quantization in this checkpoint.
+- **MTP head.** Present and preserved byte-identically. Deliberately unused.
+
+## Why this checkpoint, and the trap that disqualifies most others
+
+vLLM selects the PLE table's weight format from **`text_config.ple_embedding_dtype`**,
+as the *first* branch of `Qwen4ExpPLEEmbeddingMethod.from_quant_config`. This
+checkpoint declares `"float8_e4m3fn"`.
+
+A build that ships an FP8 PLE table **without** that declaration resolves instead
+through ModelOpt's `*.ple.*` exclude to the *unquantized* method, never registers
+the `weight_scale` parameter, and dies on load with `no module or parameter named
+'ngram_embedding.weight_scale'`. `gorbatjovy/qwen3.8-flash-next-abliterated-NVFP4-plefp8`
+is exactly this case. **Check that field before trying another build.**
+
+Chosen over better-liked alternatives because its provenance states protocols and
+repeat counts rather than adjectives. From its own `qualification-notes.md` and
+metrics files, kept in the model directory:
+
+- HarmBench, 240 genuinely-harmful behaviours, greedy: 100% compliance at reasoning
+  off / low / xhigh.
+- MMLU 82.11% → 81.93% (−0.18 pp) on an identical harness, 2,280 questions.
+- GSM8K 97.27% (1283/1319), full set, single-shot, temp 0.6 — inside the stated
+  BF16 reference band 97.12–97.50.
+- AIME26 pass@1 98.75% (237/240, **SEM 0.61 pp, 30 problems × 8 repeats**),
+  majority@8 100%, `max_tokens` 130,000, 4.9M completion tokens, stop_rate 99.17%.
+- Byte-equality audit of unchanged tensors: 1,562 tensors / 118.4 GB compared, all
+  passed, **including all 31 MTP tensors**.
+
+⚠ Two honest gaps in that evidence. The routed experts are **NVFP4 W4A4**, and
+nobody — including the publisher — has measured this checkpoint at the full 262K
+context; AIME26's 130K-token generations are the deepest evidence that exists.
+Separately, `validate_checkpoint_report.json` in the repo describes the *earlier
+BF16-PLE revision* (204 shards / 173.6 GiB), not the published FP8-PLE one.
+
+Rejected alternatives, for the record:
+`orcarouter/…-Uncensored-NVFP4` is gated (access request pending nothing — not
+requested); `nvidia/…-NVFP4` is the cleanest ModelOpt MIXED_PRECISION build but is
+not abliterated; `lovedheart/…-Pruned-RTXPRO-6000` prunes to 448 of 512 experts.
+
+## Why MTP is off
+
+Against our house graft-MTP habit, and on purpose.
+
+vLLM's own recipe for this model measured MTP on 4×H100 as **worse at every
+concurrency tested** — 8–36% lower request throughput, 32–173% higher per-token
+latency, driven by ~36% acceptance — and says do not enable it by default. Open
+issue **#55357** reports episodic 0% draft acceptance with repetition collapse
+inside thinking blocks. Open **#55496** reports ModelOpt `MIXED_PRECISION` failing
+to load FP8_BLOCK_SCALES MTP experts.
+
+Turning it on is two lines in `compose.yaml` (documented in place). If you do,
+measure it **here**, with repeats, against this seat's own baseline — the numbers
+above are someone else's hardware.
+
+## The upstream situation, as of 2026-09-13
+
+- **#53896** — model support. **Merged 2026-08-31.** In v0.29.0.
+- **#54371** — *UVA PLE-offload and Engram tensor parallelism*. **Merged
+  2026-09-09T14:32Z.** This is the offload this seat uses. **Not in v0.29.0**,
+  which was cut ~6 h earlier; present in `v0.29.1rc0` and in any nightly from
+  2026-09-10 onward.
+- **#53899** — the *older, worker-based* PLE offload. **Open and explicitly paused**
+  in favour of #54371. Do not go back to it. Its whole bug family — the TP=1
+  startup deadlock (#53960), the `pidfd_getfd` / `kernel.yama.ptrace_scope` gate,
+  the shared-CUDA-event race under async scheduling, and silently one-step-stale
+  PLE outputs under CUDA graphs — came from the separate worker process and the
+  CUDA-IPC row transfer that the UVA path does not have.
+
+Open issues worth knowing about on SM120, none of them blocking:
+
+| Issue | What it does | Our exposure |
+|---|---|---|
+| **#54173** | CUBLAS internal error / illegal memory access in the GDN path **with prefix caching** | We enable prefix caching. `FN_PREFIX_CACHING=` is the one-line rollback. |
+| **#54764** | PLE short-conv batched prefill pads every request to the batch-max query length | Why `--max-num-batched-tokens` is 8192, not 16384 |
+| **#54919** | Long prefill starves active decode for 3–7 minutes | Why context starts at 128K |
+| **#54521** | Greedy decoding non-deterministic from `persistent_topk` in prefill | Affects any A/B on this seat — establish a noise floor before comparing |
+| **#54426** | fp8_e4m3 KV on the QSA path is an unmerged RFC | Why `--kv-cache-dtype` is **not** set to fp8 here |
+
+## Raising context
+
+128K is a starting value, not a measured one. Before raising it, bisect with a
+**non-repeating** prompt — a repeated one hashes to cached blocks and never
+prefills deep, so it proves nothing. The `stacks/mog-sec` README records this the
+hard way: three successive context cuts all sized the *KV pool* while the crashes
+were governed by *processing depth*, which is a different number.
+
+The point of a ceiling is the refusal. Below it the seat serves; above it vLLM
+returns a clean 400 naming the limit, instead of the engine dying and taking every
+in-flight request with it.
+
+## Not done yet
+
+- **Pin `--kv-cache-memory` in bytes** from the first boot's budget line, replacing
+  the 0.90 ratio. Same discipline as `stacks/mog-sec` and `stacks/erp-seat`.
+- **Gateway wiring is deliberately absent.** Pointing any LiteLLM alias at this
+  seat — in particular displacing `gen` / `summarizer` / `classifier`, which is the
+  long-term intent recorded in henge item 49 — changes what every existing caller
+  receives and is the operator's call, not a deploy-time default.
diff --git a/stacks/flash-next-seat/compose.yaml b/stacks/flash-next-seat/compose.yaml
new file mode 100644
index 0000000..babfe46
--- /dev/null
+++ b/stacks/flash-next-seat/compose.yaml
@@ -0,0 +1,212 @@
+# flash-next-seat — Qwen3.8-Flash-Next (abliterated) on fv-ml1 GPU 2 (:8022).
+#
+# THE POINT OF THIS SEAT: it is the first model on the fleet whose weights do NOT
+# fit the card, and it runs anyway. Qwen3.8-Flash-Next is 176B total — a 125B main
+# model plus a 51B n-gram (PLE) lookup table — at ~6B active per token. The n-gram
+# table is a pure lookup with almost no compute, so it lives in PINNED HOST RAM and
+# the GPU reads the rows it needs directly over CUDA UVA. Resident on the card:
+# ~78 GiB of 95.6. Resident in host RAM: 47.7 GiB of 566 GB. See --engram-config.
+#
+# Serves dealignai/Qwen3.8-Flash-Next-ABLITERATED-NVFP4, revision
+# be794b990578ef3031eccf9f28e675a289a09ee9, pulled to
+# /tank/aimodels/qwen38-flash-next-abliterated-nvfp4 (126.0 GiB on disk).
+#   - routed experts (294,912 tensors, 48 layers x 512 experts) NVFP4 W4A4
+#   - PLE n-gram table FP8 E4M3, 10 `model-plefp8-*` shards + per-table scalar scale,
+#     taken from Qwen/Qwen3.8-Flash-Next-FP8 rather than re-derived
+#   - everything else source precision: self_attn, linear_attn, mlp.gate,
+#     shared_expert, hyper_connection, ple projections, visual tower, embeddings,
+#     lm_head and all 31 MTP tensors are in the quant config's `ignore` list
+#   - abliteration is a direct weight edit, no fine-tune: HarmBench 240-real-harm
+#     compliance 100% at reasoning off/low/xhigh, MMLU 82.11 -> 81.93 (-0.18pp),
+#     GSM8K 97.27% (full 1319), AIME26 pass@1 98.75% (30 x 8 repeats, SEM 0.61pp).
+#     Publisher's own reports are kept in the model dir; they state protocols and
+#     repeat counts, which is why this build was chosen over better-liked ones.
+#
+# ⚠ THE CHECKPOINT-SELECTION TRAP, recorded because it silently disqualifies most
+# community builds. vLLM picks the PLE table's format from
+# `text_config.ple_embedding_dtype` as the FIRST branch of
+# Qwen4ExpPLEEmbeddingMethod.from_quant_config. This checkpoint declares
+# "float8_e4m3fn". A checkpoint with an FP8 PLE table and NO such declaration
+# (e.g. gorbatjovy/qwen3.8-flash-next-abliterated-NVFP4-plefp8) resolves instead
+# through ModelOpt's `*.ple.*` exclude to the UNQUANTIZED method and fails to load
+# on the scale tensor it does ship. Check that field before trying another build.
+#
+# ⚠ IMAGE: must contain vLLM PR #54371 ("UVA PLE-offload and Engram tensor
+# parallelism", merged 2026-09-09T14:32Z). Verified by ancestry, not version string:
+# nightly-eed1f3d0... is +150 commits / behind_by=0 from merge commit 3116c5d0.
+# NOT in v0.29.0 — that release was cut ~6h before the merge. The older worker-based
+# offload (#53899) is PAUSED upstream in favour of this one; do not go back to it,
+# its entire bug family (TP=1 deadlock, pidfd_getfd/yama ptrace gate, shared-event
+# race, silently one-step-stale outputs under CUDA graphs) came from the separate
+# worker process that the UVA path does not have.
+#
+# ⚠⚠ MTP IS DELIBERATELY OFF, against our house graft-MTP habit. The model ships a
+# working MTP head and this checkpoint preserves it byte-identically (31 tensors,
+# audit passed) — we still do not enable it. vLLM's own recipe measured MTP on
+# 4xH100 as WORSE AT EVERY CONCURRENCY tested: 8-36% lower request throughput,
+# 32-173% higher per-token latency, ~36% acceptance, and says do not default it on.
+# Open #55357 reports episodic 0% draft acceptance with repetition collapse inside
+# thinking blocks. If you want it, it is FN_SPEC_CONFIG — but measure it here, with
+# repeats, before believing it.
+name: flash-next-seat
+services:
+  vllm-flash-next:
+    image: ${FN_IMAGE:-vllm/vllm-openai:latest}
+    container_name: ${FN_CONTAINER_NAME:-vllm-flash-next}
+    restart: unless-stopped
+    ipc: host
+    # The PLE table is 47.7 GiB of PINNED host memory. Pinned pages are not
+    # swappable and count against the process's locked-memory limit, so lift it.
+    ulimits:
+      memlock: -1
+    ports:
+      - "${FN_PORT:-8022}:8000"
+    volumes:
+      - /tank/aimodels/huggingface:/hfcache
+      - ${FN_MODEL:-/tank/aimodels/qwen38-flash-next-abliterated-nvfp4}:/model:ro
+    environment:
+      - HF_HOME=/hfcache
+      - HF_HUB_CACHE=/hfcache/hub
+      - VLLM_API_KEY=${API_KEY:-}
+      # Intentionally UNSET by default. expandable_segments has corrupted retained
+      # tensors elsewhere on this box (quant playbook §3.10) and the offload path
+      # hands UVA a pinned host allocation — not a combination anyone has tested.
+      - PYTORCH_CUDA_ALLOC_CONF=${FN_ALLOC_CONF:-}
+    command:
+      - /model
+      - --served-model-name
+      - ${FN_SERVED_NAME:-qwen3.8-flash-next-uncensored}
+      - ${FN_SERVED_NAME_THINK:-qwen3.8-flash-next-uncensored-thinking}
+      - --host
+      - 0.0.0.0
+      - --port
+      - "8000"
+      # ⚠ modelopt_fp4, NOT compressed-tensors as on the other seats here. This
+      # checkpoint's quantization_config carries compressed-tensors-shaped
+      # `config_groups` AND `quant_method: modelopt` / `quant_algo: NVFP4`
+      # (producer nvidia-modelopt 0.46.0). The ModelOpt reader is the one whose
+      # per-prefix `is_layer_excluded` honours the 13-entry ignore list that keeps
+      # attention, the shared experts, the PLE and MTP out of W4A4.
+      - --quantization
+      - ${FN_QUANT:-modelopt_fp4}
+      # ── THE WHOLE REASON THIS SEAT FITS ──────────────────────────────────────
+      # Keeps the 51B n-gram table in pinned host RAM, read by the GPU over UVA on
+      # a dedicated stream with async prefetch. Legacy VLLM_PLE_CPU_OFFLOAD=1 still
+      # works but is deprecated in favour of this flag.
+      # `embedding_across_dp` is irrelevant at TP=1/DP=1 — left at its default.
+      - --engram-config
+      - '${FN_ENGRAM_CONFIG:-{"cpu_offload": true}}'
+      # GPU 2 is DEDICATED to this seat — nothing else is resident on the card, so
+      # unlike gen/mog-sec on GPU 0/1 this ratio is not competing with a neighbour.
+      # 0.90 is the vLLM recipe's own figure for offload configurations.
+      # ⚠ PIN --kv-cache-memory IN BYTES once the first boot reports its budget
+      # line. Same lesson as stacks/mog-sec and stacks/erp-seat: a ratio sizes the
+      # KV calculation but does not bound peak activation, and it silently yields a
+      # different cache depending on what is resident at start time.
+      - --gpu-memory-utilization
+      - ${FN_GPU_MEM_UTIL:-0.90}
+      # ⚠ 128K TO START, NOT the native 262,144 the checkpoint advertises, and the
+      # gap is deliberate. Two open upstream issues make depth the risky axis here:
+      # #54764 (PLE short-conv batched prefill pads every request to the batch-max
+      # query length, so one deep request inflates the whole step's activation) and
+      # #54919 (long prefill starving active decode for 3-7 minutes). The mog-sec
+      # lesson applies directly: what the KV pool can HOLD and what the card can
+      # PROCESS at depth are different numbers, and sizing to the pool never fixed
+      # a depth-driven crash. RAISE THIS ONLY AFTER BISECTING with a NON-REPEATING
+      # prompt — a repeated one hashes to cached blocks and never prefills deep.
+      - --max-model-len
+      - ${FN_MAX_MODEL_LEN:-131072}
+      - --max-num-seqs
+      - ${FN_MAX_NUM_SEQS:-16}
+      # 8192 rather than the 16384 used elsewhere, for the #54764 padding reason
+      # above: a chunk's activation cost here scales with the batch's DEEPEST
+      # request, not its average. Costs prefill throughput, costs no context.
+      - --max-num-batched-tokens
+      - ${FN_MAX_NUM_BATCHED_TOKENS:-8192}
+      - --trust-remote-code
+      - --dtype
+      - auto
+      # Hybrid backbone: 36 of 48 layers are Gated DeltaNet (linear attention), the
+      # other 12 are Qwen Sparse Attention. The model's own config asks for
+      # mamba_ssm_dtype float32.
+      - --mamba-cache-dtype
+      - float32
+      # ⚠ MANDATORY-ISH: Qwen4Exp RAISES NotImplementedError on mamba_cache_mode
+      # "all" and tells you to use "align". "align" is already the implicit default
+      # when prefix caching is on, but it is stated here so that turning prefix
+      # caching off cannot silently select a mode the model rejects.
+      - --mamba-cache-mode
+      - ${FN_MAMBA_CACHE_MODE:-align}
+      # ⚠ FIRST THING TO TURN OFF if you see CUBLAS_STATUS_INTERNAL_ERROR or an
+      # illegal memory access: open #54173 is exactly that, in the GDN path, WITH
+      # prefix caching. On by default because every vLLM recipe config for this
+      # model enables it.
+      - ${FN_PREFIX_CACHING:---enable-prefix-caching}
+      - --enable-chunked-prefill
+      # ⚠ KV CACHE LEFT AT AUTO — do NOT copy `--kv-cache-dtype fp8` from the gen or
+      # mog-sec seats. fp8_e4m3 KV on this model's QSA path is an unmerged RFC
+      # (#54426: working patch, one machine, author asking for corroboration).
+      #
+      # Recipe sets this in every configuration; autotune has no value here and
+      # costs startup time.
+      - --no-enable-flashinfer-autotune
+      - --limit-mm-per-prompt
+      - '${FN_LIMIT_MM:-{"image": 4}}'
+      # ⚠ MANDATORY. This checkpoint's preprocessor_config.json declares
+      # size.longest_edge = 16777216 px (4096x4096), which expands to ~16,384 image
+      # tokens — one image eating 12% of a 128K context, and enough to fail startup
+      # on builds that enforce the text-vs-ids token count check. Same trap already
+      # documented on stacks/mog-sec. 4194304 = 2048x2048 -> ~5,125 tokens.
+      - --mm-processor-kwargs
+      - '${FN_MM_PROCESSOR_KWARGS:-{"size": {"longest_edge": 4194304, "shortest_edge": 65536}}}'
+      - --reasoning-parser
+      - ${FN_REASONING_PARSER:-qwen3}
+      # The Qwen3.8 chat template defaults to `xhigh`, where reasoning length grows
+      # with conversation depth and has a long tail — measured on the 27B sibling at
+      # 4,529-5,532 chars spiking to 9,261. `medium` is the seat-level default;
+      # per-request override via chat_template_kwargs still works.
+      - --default-chat-template-kwargs
+      - '{"reasoning_effort": "${FN_REASONING_EFFORT:-medium}"}'
+      - --enable-auto-tool-choice
+      # qwen3_xml, per the vLLM recipe for THIS model — not the qwen3_coder parser
+      # the mog-sec seat uses.
+      - --tool-call-parser
+      - ${FN_TOOL_CALL_PARSER:-qwen3_xml}
+      # ── NO --speculative-config LINE, ON PURPOSE ─────────────────────────────
+      # Speculative decoding is absent rather than disabled-by-a-flag, because
+      # there is no honest "off" value for --speculative-config and a placeholder
+      # flag in this slot is the kind of thing that gets copied forward and
+      # misread. To turn MTP on, add these two lines here and nothing else:
+      #     - --speculative-config
+      #     - '{"method": "mtp", "num_speculative_tokens": 3}'
+      # Read the MTP warning in the header first; the measured default is off.
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids:
+                - "${FN_GPU_ID:-2}"
+              capabilities:
+                - gpu
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      interval: 30s
+      timeout: 10s
+      # ⚠ LONG. Cold boot reads 126 GiB from /tank and pins 47.7 GiB of it; the
+      # closest published single-card timing is ~8.5 min load on a 96 GB card.
+      # 1800s so a slow first boot is not mistaken for a failed one.
+      start_period: 1800s
+      retries: 3
+    networks:
+      - tnet
+    labels:
+      - homepage.group=AI - Inference
+      - homepage.name=Qwen3.8-Flash-Next (uncensored)
+      - homepage.icon=mdi-lightning-bolt-outline
+      - homepage.description=176B/6B-active MoE, abliterated, NVFP4 + 48GB n-gram table offloaded to host RAM — fv-ml1 GPU 2
+      - homepage.href=http://10.251.50.54:${FN_PORT:-8022}/docs
+networks:
+  tnet:
+    name: traefik-net
+    external: true