esh-pfi-infrastructure/stacks/flash-next-seat/README.md

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

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.