memory: snapshot — the Ada inference server is a stripped used R750xa

Dell R750xa JPJ1ZP3, 2x RTX 6000 Ada to be fitted, ComfyUI's new home at NH3. Diffing
Dell's factory CSV against the reseller invoice shows four downgrades: half the RAM, the
2400 W PSUs, and the GPU risers, cables and high-performance fans all absent.

Records the resolved GPU power chain, correcting my own first answer: the chassis' CPU
8-pin cabling is the right source type and NVIDIA 930-00030-1546-000 bridges it to the
card's 12VHPWR, so the PCIe-type RCCWC I first proposed is withdrawn. Also closes the NVMe
question — the backplane is SAS/SATA only — and notes that free PCIe slots may moot it.

Auto-archival fired at 308 lines; seven entries moved to archival-memory.md. The 250-line
target was not reached because the guards hold nearly everything else back as under 14 days
or carrying open deferred work.

persistent-memory.md

@@ -119,6 +119,14 @@ _As of 2026-09-01 — **the GX10 is on the operator's desk, NOT racked. Standing
   one command afterwards (`playbooks/gx10-rack-network.yaml`, VLAN 50, static `10.100.50.60`).
   ⚠ Triton has no sm_121 support; compiled deps are per-arch unknowns.
   → `persistent-memory.d/2026-09-01-pfi-gx10-onboarding.md`
+- **▶ ADA INFERENCE SERVER (R750xa `JPJ1ZP3`) — awaiting an iDRAC inventory.** 2× RTX 6000
+  Ada to be fitted; ComfyUI's new home at NH3. **Order regardless: 8× `M04W6` RDIMM (restores
+  factory 256 GB) and 2× NVIDIA `930-00030-1546-000` 12VHPWR adapters.** Everything else waits
+  on the inventory — the invoice omits risers, GPU cables and the 6 high-performance fans, but
+  omission is not proof of removal. **Add free-PCIe-slot inventory to the pull** — an NVMe
+  add-in card would bypass the SAS/SATA backplane and moot the drive-bay question.
+  ⚠ ~1 kW loaded — same class as the box that tripped the breaker; pick the NH3 circuit before
+  racking. → `persistent-memory.d/2026-09-01-ada-inference-server-r750xa.md`
 - **⏸ RUN 3c STILL HELD — but the plan has changed.** Config `/tank/erp-tune/run-03c.json`
   validated, relaunch is one command on ana-ml2. **It is now intended to move to the GX10
   instead**, which is the power answer rather than a power triage. Do not relaunch on ana-ml2
@@ -147,6 +155,7 @@ _As of 2026-09-01 — **the GX10 is on the operator's desk, NOT racked. Standing
 
 ## Recent decisions
 
+- `[2026-09-01]` **The Ada inference server is a used Dell R750xa (`JPJ1ZP3`) and the reseller stripped four things Dell shipped** — half the RAM, the 2400 W PSUs, and the GPU risers/cables/fans are absent from the invoice. Card is **RTX 6000 Ada**, not L40S. GPU power chain resolved via NVIDIA `930-00030-1546-000`. NVMe in the drive bays is CLOSED (SAS/SATA backplane). → `persistent-memory.d/2026-09-01-ada-inference-server-r750xa.md`
 - `[2026-09-01]` **pfi-gx10 onboarded headless — and it is the intended new home for run 3c, which died on a tripped breaker.** GB10/sm_121/aarch64, 121 GB unified. NOT racked yet. Bare of any CUDA stack; probe throughput before porting. → `persistent-memory.d/2026-09-01-pfi-gx10-onboarding.md`
 - `[2026-09-01]` **Ada migration is `zfs send` (branch a) — branch (b) was never available because irv-ml1 keeps its eight services.** 99 MB/s measured; ~3.9 h. Also records the two-boxes confusion: the Ada box and the GX10 are DIFFERENT machines. → `persistent-memory.d/2026-09-01-ada-migration-branch-a.md`
 - `[2026-09-01]` **Matrix: Synapse 1.120→1.159, appservice namespace opened, `/_synapse/admin` closed to the internet, alias convention ratified.** Schema migrations are one-way; push is `event_id_only` and assembled on-device. → `persistent-memory.d/2026-09-01-matrix-upgrade-and-hardening.md`
@@ -251,7 +260,6 @@ _As of 2026-09-01 — **the GX10 is on the operator's desk, NOT racked. Standing
 
 - `[2026-08-16]` **Abliterated models go CATATONIC at the hard refusal edge — silence, not a decline.** Abliteration removes the refusal *direction*, so at the genuine hard edge the model neither refuses nor complies → empty/degenerate output. Durable measurement consequence: a refusal probe MUST score EMPTY as a verdict distinct from REFUSAL and COMPLY (`services/refusal-probe/probe.py` does). Operator accepted it as out-of-scope; do not chase.
 
-- `[2026-08-16]` **Fable-Fusion 711 cuts cold-framing refusals 92.5% → 15.8%; refusal is MONOTONIC IN FRAMING, and DS v1.0's problem is that she was never abliterated.** brokkr-smithy-dev supplied the framing that reproduces (`01M05M48R4RSZF9D8KT7RR55EJ`): a **bare assistant-mode instruction** — no character card, no permission preamble. Three-arm A/B, same harness, same classifier: permission framing **DS 0.0% / FF 0.0%** (n=75); plain character cards **DS 1.4% / FF 0.0%** (n=74); bare instruction **DS 92.5% (37/40) / FF 15.8% (6/38)**. Per-axis DS→FF: incest 100→20, non-con 100→20, bestiality 100→25, necrophilia 100→40, gore 100→**0**, consensual 80→20, dubcon 80→**0**, self-harm 80→**0**. DS refused **25/25** on the five axes brokkr flagged. Root cause: `ReadyArt/Dark-Scarlett-v1.0-27B` is a plain finetune of stock `Qwen/Qwen3.6-27B` carrying **NO abliteration** — the base refusal machinery is intact, so cold prompts revert to safety-tuned Qwen3.6. FF is Heretic-**ablated** (structural), which is why it holds. ⚠ **RETRACTED 2026-08-16 — my "arm-3 92.5% exceeds brokkr's 62.5%" comparison was INVALID.** His diff against his own artifact showed my `battery-instruct.yaml` reproduces only his **`creative` class — 8 of 16 axes**; it dropped all 5 `operational` (violence/incite, crime/fraud, cyber/malware, selfharm/methods, privacy/stalk) and all 3 `meta` (meta/sysprompt, meta/ignore, meta/dan), and added 2 controls he never had, at k=5 vs his k=2. **His 62.5% pools all 16 axes; my 92.5% is creative-only — different denominators, not a delta.** Cause: I rebuilt his shape from his *message*, and the `class` field lives in the artifact, not the prose. **Lesson: reconstructing a peer's instrument from their description reproduces what they described, not what they ran — diff against the artifact before claiming comparability.** ⚠ **Known battery bug left unfixed for comparability:** DS's arm-3 control gate failed at 11% because `ictrl-reunion` pairs "explicit / do not fade to black" with *brothers*, which DS reasonably read as an incest request; FF did not. `ictrl-storm` is the clean control. Commit `b9e68c3`.
 
 - `[2026-08-16]` **MTP works on Fable-Fusion AND survives RP temperatures — my earlier caution was wrong.** vLLM resolved `Qwen3_5MTP`, loaded the drafter, shared embedding + `lm_head` — the capability DS's seat never had because our quant dropped her MTP tensors. Measured over the full probe workload (~163k draft windows at temp 0.7–1.0): **47.0% acceptance** (229,169/487,725), 1.41 extra tokens/window, per-position 68.3/43.6/29.1%, **~80.6 tok/s** decode at temp 1.0. I had recorded a caution that the card's 1.56× was greedy-measured and acceptance would fall at RP temps — **it did not**; 47.0% matches the gen seat's 47.7% and beats the card's own 33% at depth 5. Depth 3 is right.
 
@@ -273,23 +281,19 @@ _As of 2026-09-01 — **the GX10 is on the operator's desk, NOT racked. Standing
 
 - `[2026-08-15]` **RP-seat direction: KEEP MeroMero on `char-rp`; Artemis-31B rejected; next move is Dark-Scarlett on a Qwen3.8 base when it lands (operator).** Evaluated `TheDrummer/Artemis-31B-v1.1` — mechanically a drop-in (same `google/gemma-4-31B-it` base, identical 1188-tensor/356-vision census, same missing-`preprocessor_config.json` trick), so it's purely a quality call, and our own survey already ranked MeroMero **#1** vs Artemis **#6**; Artemis is also unlicensed and its author deprioritizes correctness + warns of token-banning-for-stability, which fights char-rp's tool-calling requirement. **MTP verified impossible on both** (Gemma-4 has no MTP head at all — base/MeroMero/Artemis are all MTP=0; no finetune can add one). **But speculative decoding IS reachable on a Gemma-4 seat via a DETACHED drafter** — vLLM 0.24 supports `eagle3` + `gemma4_mtp`, and real drafters exist: `google/gemma-4-31B-it-assistant` (0.94 GB, 4-layer, 761K dl), `RedHatAI/gemma-4-31B-it-speculator.eagle3` (4.47 GB), `AEON-7/…eagle3-NVFP4` (3.53 GB). ⚠ all list their verifier as **stock** gemma-4-31B-it, not an RP finetune, so acceptance against MeroMero is unmeasured and likely well below the gen seat's ~48%. UNTESTED — parked, ~45 min to measure, needs GPU0 headroom (card is at 94.4/97.9 GB). **Why the Dark-Scarlett 3.8 plan is the strong one:** DS is Qwen3.6-based today, so a 3.8 respin lands on the *gen seat's* architecture → native MTP returns and the whole mixed NVFP4+FP8 recipe + graft ports directly. Watch two things on arrival: `from_pretrained` **silently drops MTP heads during finetuning** (verify 15 `mtp.*` tensors in the index; graft from stock if absent), and DS v1.0 required the `Qwen3_5ForConditionalGeneration` **wrapper class** to save a config vLLM/SGLang accept. Both in `docs/pfi/model-quantization-playbook.md`.
 
-- `[2026-08-15]` **Quant lessons consolidated into `docs/pfi/model-quantization-playbook.md` — the durable home; read it BEFORE any requant.** Survey found quant knowledge scattered across 18 files in 4 trees, with **three** documents having independently written overlapping "landmines" sections (the loader-class trap alone was rediscovered 3×). Playbook owns the **transferable** lessons (scheme choice, landmines, acceptance gate + its 3 measurement traps, hardware/co-residency); per-model artifacts are demoted to worked examples that link up. Carries a **superseded-claims table** — which immediately earned itself: the heretic2 runbook's "use modelopt, compressed-tensors can't load the BF16 MTP" is **false** (the cause was the missing `re:^mtp.*` ignore, not the format) and would have sent the next session down the modelopt dependency-hell path; that runbook now carries a stale-warning header. Maintenance rule in `CLAUDE.md`: model-agnostic → playbook, model-specific → stays put, wrong claim → dated superseded row, never a silent edit. Motivated by Qwen3.8 having just released — the next model swap needs a requant. Commit `a91cc3f`.
 
-- `[2026-08-15]` **Operator ruling: the gen seat's +1.7% perplexity is an acceptable price for the speed — SETTLED, don't re-litigate.** Precise attribution for future reasoning: it is the **activation-quantization** cost (W4A4 MLPs + FP8 attention vs BF16 activations), not an MTP cost — PPL was measured with speculative decoding **off** on both builds, so MTP was not in the loop. Turning MTP off would not recover it; only reverting the quant would (rollback = one `.env` line, old build intact at `…/qwen38-27b-uncensored-nvfp4`).
 
-- `[2026-08-15]` **gen seat requanted to mixed NVFP4+FP8 (+18% decode) + char-rp Gemma-4 tool-calling fixed.** The queued "W4A8" (NVFP4 weights + FP8 activations) is **not servable** — vLLM 0.24 allows NVFP4 weights with only A16 or A4; FP8 activations ValueError at load, and `CompressedTensorsW4A8Fp8` is INT4-weights + sm90-exact (closed on Blackwell twice). FP8 must enter **per-layer-group**. Also: the handoff's "~68 tok/s" baseline didn't reproduce — cache-busted, the incumbent already did **80.12** (≈ the stated W4A8 target), so the premise needed re-measuring before any work. Shortcut: `unsloth/Qwen3.8-27B-NVFP4` was already on-box → served as a probe, measured **+19.1% at identical acceptance**, which both proved the gain was real and handed over the reference recipe. Replicated it on the abliterated weights → **80.12→94.53 tok/s, acceptance unchanged, +1.7% PPL, abliteration 4/4, weights −19%**; surface 6/6 live, 7 aliases routing. char-rp had **no** tool parser at all (every tools request 400'd) → `gemma4` tool + reasoning parser + a **mandatory** `enable_thinking:false` (the parser defaults it True → null `content` for all RP prose; proven byte-identical prompt before deploying). Commits `b8f0f4c`, `74f596b`. Foot-guns banked (llm-compressor prunes unmatched `ignore` entries → the 0%-MTP bug, **fired on this run**; prompt_logprobs uniform under spec-decode; 0600 `.env` silently no-ops compose; GPU0 is zero-sum). → `persistent-memory.d/2026-08-15-gen-seat-mixed-requant.md`
 
-- `[2026-08-15]` **Uncensored gen seat: JonathanColetti/Qwen3.8-27B-Uncensored deployed as `gen-seat`/`vllm-gen` (NVFP4 W4A16 + grafted MTP, 262K); 7 aliases repointed; the definitive `re:^mtp.*`-ignore fix.** 0%-MTP-on-quant (twice) was NOT the abliteration/scheme — the grafted bf16 MTP was missing from `quantization_config.ignore` (vLLM loaded it as quantized → uninitialized). Full arc, the working pipeline, VRAM budget, unsloth speed decomposition, modelopt dead-end. → `persistent-memory.d/2026-08-15-uncensored-gen-seat.md`
 
 - `[2026-08-10→12]` **secrets-broker: per-box Vaultwarden credential store SHIPPED + consumer-confirmed.** `secret` CLI (`put/get/list/rm/backfill`, bw-backed) on `~/.local/bin`; 25 nh3-dev secrets backfilled + round-trip-verified; `rm` + new-namespace warning added post-launch; standing "vault is the credential source of truth" directive now global. → `persistent-memory.d/2026-08-12-secrets-broker.md`
 
 - `[2026-08-09→10]` **dots.tts (rednote-hilab) TTS burn-in on irv-ml1 + canonical voice corpus built (`voices/`).** Operator-directed eval to potentially replace chatterbox-fast. **dots.tts VERIFIED real** (canonical HF ns `dots-studio/`, `rednote-hilab/dots.tts-*` redirects there; Apache-2.0; PyPI `dots.tts` 0.2.1; 2B continuous-AR = semantic enc + Qwen2.5-1.5B LLM + flow-matching acoustic head over 48kHz AudioVAE; zero-shot clone from wav+transcript). **Runs on Ampere 3090** (sm_86, bf16, no fp8 dep); **optimized RTF 0.22** at num_steps=10 (`from_pretrained(..., optimize=True)` CUDA graphs — raw unoptimized was 1.21), **~6GB VRAM**, 48kHz, streams (`generate_stream`). Venv+cache at `irv-ml1:/home/lkraven/dots-tts` (~10GB). **Operator design calls:** SGLang Omni serving (OpenAI `/v1/audio/speech`), transcribe-refs-first, `soar` variant. ⚠ Omni serves soar but its continuous-batching + streaming opts are **mf-only** (soar = single-request) — non-issue for ratatoskr's single-consumer RP surface. **KEY FINDING — dots is highly sensitive to an accurate AND sentence-bounded reference transcript:** mismatched transcript → 0.16s collapse; over-long/messy transcript → reference-audio BLEEDS as an output prefix; mid-clause trim → dangling-word leak (glados "we'll", emmie "And,"). RECIPE (baked into `voices/derive.py`): trim ref to a clean ~6–10s clip ending on a sentence boundary + accurate transcript of exactly that clip. **CANONICAL VOICE CORPUS** stood up in eshpfi `voices/` (operator idea): engine-agnostic `canonical/<v>.wav` + `transcripts/<v>.txt` → per-engine ref sets DERIVED by `derive.py` reading `engines.yaml` profiles (dots/chatterbox/zonos); canonical wavs git-tracked (small/curated), `derived/` gitignored. **4 voices optimized + verified CLEAN for dots: donut, glados, emmie, miranda** (glados canonical is low-SR 16kHz — flagged upgrade candidate). ⚠ GPU GOTCHA: irv-ml1 native CUDA orders **A6000=device0** (ComfyUI-full) — pin the 3090 with `CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0`; and `PYTORCH_CUDA_ALLOC_CONF=expandable_segments` CONFLICTS with `optimize=True` CUDA graphs (curr_block error). Booths: `dots-vs-chatterbox`, `dots-voices-optimized`. **SHIPPED 2026-08-10:** operator A/B verdict "dots is very good" → containerized as a **thin FastAPI wrapper over DotsTtsRuntime** (chosen over SGLang Omni — Omni's batching is mf-only, unneeded for ratatoskr's single consumer; wrapper is SERIALIZED one-gen-at-a-time via a threading.Lock, Omni+mf = parked API-compatible escalation if multi-consumer ever lands). **LIVE on irv-ml1:8198** (`local/dots-tts:v1`, OpenAI `/v1/audio/speech` + `/health` + `/v1/voices`, container healthy, both stream + non-stream verified CLEAN, 4 voices donut/glados/emmie/miranda) alongside chatterbox :8197 (nothing repointed). Stack = `stacks/dots-tts/` (Dockerfile/app.py/compose/.env.example/README). ⚠ CONTAINER GOTCHA: `optimize=True` (torch.compile/inductor/triton) needs a **C compiler at RUNTIME** — slim image must `apt install build-essential` or model-load dies "Failed to find C compiler" (host venv had gcc ambient, masking it); persist `TORCHINDUCTOR_CACHE_DIR` to a mounted dir or every restart re-JITs ~5min. Corpus home = eshpfi `voices/` (operator ruled keep-here). **REMAINING: ratatoskr client cutover** to :8198 `/v1/audio/speech` (Phase-2 tail, peer-coupled — draft the ask). [[reference_chatterbox_fast_repo]] [[reference_zonos_tts_stack]] [[reference_verify_hf_repo_ids_before_pull]]
 
-- `[2026-08-05]` **Fleet CI resilience flip (`DEFAULT_ACTIONS_URL=self`) — attempted end-to-end, PARKED on a runner action-fetch auth blocker; infra-ops to research it (operator-directed, deferred, NOT now).** 7 gitea action mirrors staged public+populated (orgs `actions`+`astral-sh`); the flip resolves `uses:` correctly but act_runner v0.6.0 can't authenticate its fetch to gitea 1.26 ("Invalid username or token. Password authentication is not supported"). Reverted (CI back on github default); `REQUIRE_SIGNIN_VIEW=false` KEPT as a standing change (operator, internal WG net). Full endeavor, the reliable nh3-dev-egress + git-SSH mirror method, exact config state, smoke method, and next step → `persistent-memory.d/2026-08-05-ci-flip-parked.md`
 
-- `[2026-07-31]` **muninn-gate (#377 ingestion front door) BUILT + DEPLOYED + healthy on corviduo-dev:8090.** First-boot acceptance passed (watcher:running:true proves ingestion_root byte-identity); submit path deferred to the mimir-inbox era. Full wiring (uid-1000, state-volume mount, staging path-agreement, BuildKit-secret build, deferred repoint + operational guards) → `persistent-memory.d/2026-07-31-muninn-gate-deploy.md`
 
 _223 older entries archived to archival-memory.md._
+_Older entries archived to archival-memory.md._
+
 ## Tried and abandoned
 
 - `[2026-08-25]` **Four throughput levers measured and killed — do not re-chase.** (1) **Fused MoE / `grouped_mm`** — 0.9% *slower* than the Python loop and dense GEMM is only 7.9% of the step, capping the whole category near 10%. (2) **CUDA graphs / `torch.compile` over the expert loop** — the two-term scaling fit closed with residuals under 3ms and needed NO constant term, so there is no fixed per-batch cost to amortise; 3,840 expert-GEMM launches per forward are not what we pay for. (3) **`liger` fused linear CE** — the chunked CE measured **1.1% of the step** forward, ~3% with recompute. A tidy-up, not a lever. (4) **Selective gradient checkpointing** — ~2% of a post-fix step, real bug surface. Also: **token-budget batching is dead by the same fit** — with no constant term, total time over a fixed set of widths is invariant to how you group them; only the widths matter, which is exactly why bucketing works and repacking does not.