frank-primary: roll back to qwen FP8 + MTP4 (2339fae trio)

DFlash2 experiment declared failed 2026-10-01: the drafter serves and
drafts (12.8 tok/s proposed) but acceptance is pinned at 1.0-1.38 on ALL
text classes including verbatim repetition — the aux-hidden-state capture
semantics are wrong (single-stream entering-state vs post-layer/comb is
the untested alternative). At ~1.2 acceptance the 8-token verify tax makes
decode 5x SLOWER than the 9.9-10.0 baseline (1.6-1.8 tok/s measured).

Restores the verified 37-40 tok/s shape: qwen3.8-flash-next FP8, MTP4,
prefetch off, 200k x 3 seqs, fp8 KV.

scripts/frank-primary.env

@@ -1,53 +1,34 @@
 # frank-primary.env — tcclaviger RDNA4 vLLM production config (dankserver :7770).
 #
-# 2026-09-30 cutover from orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8 (MTP4) to
-# Intel/GLM-5.3-Flash-W4A16-AutoRound, the first payload this fleet has served
-# through the fork's W4A16 expert-offload path. ROLLBACK: the pre-cutover qwen
-# state is commit 2339fae ("frank-primary: fix the SIGPIPE race in both
-# image-digest gates") — restore scripts/ from it and deploy; the qwen payload
-# (173 GiB) and the R9V state dirs are untouched on disk.
+# 2026-09-29 cutover from the R9V radiance-vLLM fork (its launcher/shim/state
+# machinery is retired; /home/dank/r9v-state/{q8xl,uncensored} stay on disk as
+# the rollback target, and git tag frank-primary-r9v-q8-20260929 is the last
+# pre-cutover commit). The engine is the tcclaviger RDNA4 vLLM fork — the same
+# digest-pinned image the 2026-09-29 tuning campaign measured — serving
+# orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8 (~172.8 GiB, 131 shards) with
+# the MTP4 drafter on the two R9700s.
 #
-# QUALIFIED 2026-09-30 by boot probe + bench against the live engine:
-#   * THE 35 tok/s TARGET IS NOT MET: decode 9.9-10.0 tok/s, prefill 918 tok/s
-#     (warm, 2.6k-token prompt) at 131,072 ctx x 2 seqs. See the runbook's
-#     "Why 35 tok/s is out of reach on this hardware" — the bound is the fork's
-#     expert-gather read path (8.2 GB/s effective vs the link's 22.74 GB/s),
-#     the same structural gap the 2026-09-29 qwen campaign measured.
-#   * Arena 14.51 GiB = 2,383 slots x 6.234 MiB per rank; 12,096 host-backed
-#     experts per rank, 73.64 GiB pinned. Live per-layer telemetry
-#     (R4D_LRU_TELEMETRY=1, 2026-09-30): hit rate 0.53-0.66, ~500-730 installs
-#     vs ~2,000 routed per layer over the window.
-#   * --expert-precise-memory is ON: it replaces the 6.25 GiB default
-#     compile-headroom figure with the registry's fitted 5.46 GiB, buying
-#     1.43 GiB of cache (13.08 -> 14.51 GiB). Decode measured flat across that
-#     change (10.03 -> 9.88 tok/s), which is the evidence that the hit rate
-#     saturates near 0.6 and residency is no longer the binding term.
-#   * KV dtype stays bf16/AUTO — fp8 KV is NOT legal on this model. It disables
-#     _use_rocm_sparse_triton() in the ROCM_AITER_MLA_SPARSE backend (the
-#     predicate requires a non-fp8 cache), and that rope-free Triton path is the
-#     only MLA decode available on gfx12: aiter's MLA decode op is registered
-#     under @if_aiter_supported, i.e. gated on CDNA>2. With fp8 KV the MTP draft
-#     step dies with "'_OpNamespace' 'vllm' object has no attribute
-#     'rocm_aiter_mla_decode_fwd'".
-#   * MTP4 spec decode is ABSENT DELIBERATELY, unlike the qwen shape. It WORKS
-#     here (the MTP head loads, architecture resolves to Glm5NextMTPModel, and
-#     acceptance is excellent: mean accepted length 3.76-4.90, per-position
-#     0.92/0.85/0.69/0.58, 69-97%) — but it buys nothing on an offload-bound
-#     deployment: measured decode 9.6-11.2 tok/s (vs 10.0 without) while prefill
-#     COLLAPSED 918 -> 255 tok/s. The verification forward gathers the union of
-#     the batch's experts, so spec decode does not amortize a residency-bound
-#     gather the way it amortizes compute. It also costs 2.6 GiB of arena.
-#     Reinstate only with a fresh A/B on a non-offload-bound shape.
-#   * PREFETCH IS ABSENT BY LAW, unchanged from the qwen campaign: expert-cache
-#     telemetry measured the prefetcher at 36% precision and deleting the flag
-#     bought +40-51% decode. Never re-add --expert-prefetch without a fresh A/B.
+# Every number here was QUALIFIED by boot probe on 2026-09-29 (not inherited):
+#   * MAXLEN 200000 x SEQS 3 (600k total) at KV q8/q8 fits with headroom:
+#     the engine measured "GPU KV cache size: 729,545 tokens; Maximum
+#     concurrency for 200,000 tokens per request: 3.65x" at KV_BYTES 7.2 GiB
+#     per rank. KV_BYTES is PER RANK — sizing it to the global requirement
+#     OOMs the worker (measured twice: 16 GiB and 9 GiB both die with ~7.5 GiB
+#     free; 7.2 GiB boots).
+#   * --kv-cache-dtype fp8 is the fleet's q8/q8 KV ask (e4m3 for BOTH K and V
+#     — the stock flag quantizes them together, which is exactly what was
+#     requested here). Accepted by the attention path; qualified with a
+#     completion probe.
+#   * PREFETCH IS ABSENT BY LAW: expert-cache telemetry measured the
+#     prefetcher at 36% precision (36,720 slabs prefetched, 13,369 served) —
+#     roughly half the gather traffic was waste; prefetch off is the tuned
+#     incumbent and the +40-51% decode came from deleting the flag. Never
+#     re-add --expert-prefetch without a fresh A/B.
 #   * TunableOp stays OFF: CLAV_TUNABLEOP_SWEEP silently skips under expert
 #     offload unless CLAV_TUNABLEOP_WITH_OFFLOAD=1, and forcing it corrupts
 #     numerics (repetition loops, early halts, invalid measurements).
-#   * KV is NOT capped explicitly: the fork's plan reserve (1.76 GiB at 131,072)
-#     already covers what max_model_len needs for this shape. It does NOT for an
-#     MTP shape — see the runbook's fork-bug section and
-#     scripts/patches/gpu_worker-kv-cap.patch.
+# Decode at the tuned shape: 40.57/37.44/37.57 tok/s (166/1087/4064 ctx) at
+# the 32k window; 42.94/34.55/36.09 at this 200k shape (arena 6,032 slots).
 #
 # HIP reads bare env vars TEMP, HOST, GPU_MAX_HEAP_SIZE — every key here is
 # prefixed FRANK_PRIMARY_ so none can leak.
@@ -55,57 +36,26 @@
 # --- runtime ----------------------------------------------------------------
 # Digest-pinned: the floating :latest tag moved once already. Local alias
 # qwen-fp8-runtime:tuned-20260929 (image ID 3322dbbbc96d) points at the same
-# bytes in rootless storage. Same image as the qwen cutover — the W4A16 path
-# (INC scheme -> R4dW4a16MoEMethod, libr4d grouped W4A16 GEMM) ships in it.
+# bytes in rootless storage.
 FRANK_PRIMARY_IMAGE=docker.io/tcclaviger/vllm@sha256:da4029666668b90a0386c1ed23ea370c79916ad4e1a6ad1dd9db098b9fa069ea
 
 # --- payload ----------------------------------------------------------------
-# Intel AutoRound W4A16: symmetric int4, group 128, packing "auto_round:auto_gptq".
-# quantization_config.quant_method is "auto-round", which the fork overrides to
-# "inc" (inc.py:364) — that is what routes the MoE to R4dW4a16MoEMethod instead
-# of the compressed-tensors path's Triton experts, which has no offload recipe.
-# 34 numbered shards + model_extra_{conv,tensors}.safetensors, 113,074 tensors.
-FRANK_PRIMARY_MODEL_DIR=/home/dank/models/GLM-5.3-Flash-W4A16-AutoRound
-FRANK_PRIMARY_MODEL_SHARDS=34
+FRANK_PRIMARY_MODEL_DIR=/home/dank/models/Qwen3.8-Flash-Next-Uncensored-FP8
+FRANK_PRIMARY_MODEL_SHARDS=131
 
 # --- identity ---------------------------------------------------------------
 # The harness models.yml id stays frank-primary (provider 192.168.0.235:7770).
-FRANK_PRIMARY_CONTAINER=frank-primary-glm
+FRANK_PRIMARY_CONTAINER=frank-primary-qwen
 FRANK_PRIMARY_SERVED_NAME=frank-primary
 FRANK_PRIMARY_PUBLISH=7770:7770
-# Containers removed before every start: the pre-cutover qwen identity (it holds
-# the same port and GPUs), the retired R9V identity, and the ad hoc experiment
-# identity.
-FRANK_PRIMARY_LEGACY_CONTAINERS="frank-primary-qwen frank-primary-r9v qwen-fp8 glm-probe"
+# Containers removed before every start: the retired R9V identity and the ad
+# hoc experiment identity (both hold the GPUs / name registrations).
+FRANK_PRIMARY_LEGACY_CONTAINERS="frank-primary-r9v qwen-fp8"
 
 # --- qualified engine shape ---------------------------------------------------
-# 131,072 x 2 seqs. The window is NOT chosen for capacity: the engine's own
-# plan reserve is one max_model_len request, and below ~92k tokens the fork's
-# reserve model (13,600 B/token + a small mamba term) falls short of what the
-# engine actually needs for this model's per-sequence KDA state, which makes the
-# boot refuse ("2.07 GiB needed vs 1.74 GiB available"). 131,072 clears it with
-# the plan's own cap intact. KV cache as built: 138,561 tokens, 1.06x
-# concurrency for a 131,072-token request.
-FRANK_PRIMARY_MAXLEN=131072
-FRANK_PRIMARY_SEQS=2
-# 0.99 fails boot ("Free memory on device cuda:1 ..."); 0.98 leaves the
-# compile/capture headroom the plan models.
+FRANK_PRIMARY_MAXLEN=200000
+FRANK_PRIMARY_SEQS=3
+FRANK_PRIMARY_KV_BYTES=7730934784
+FRANK_PRIMARY_KV_DTYPE=fp8
+FRANK_PRIMARY_SPEC=4
 FRANK_PRIMARY_UTIL=0.98
-# Prefill chunk. 2,048 is the qualified value; the expert union a chunk routes
-# to saturates quickly, so the chunk size is an install-amortization knob, not a
-# residency one. Measured 2026-09-30: NBATCH 8,192 boots (arena 12.51 GiB) and
-# leads to the SAME warm prefill (922 vs 918 tok/s) — no reason to pay its
-# 2 GiB of profile buffer.
-FRANK_PRIMARY_NBATCH=2048
-# GLM's own tool-call dialect. Without --enable-auto-tool-choice the endpoint
-# rejects tool_choice "auto" outright ("\"auto\" tool choice requires
-# --enable-auto-tool-choice and --tool-call-parser to be set"), which breaks
-# every tool-using agent on the endpoint. Verified 2026-09-30: a get_weather
-# prompt returns tool_calls with finish_reason "tool_calls".
-FRANK_PRIMARY_TOOLPARSER=glm47
-# GLM's reasoning dialect. Without it the model's thinking leaks into `content`
-# (measured: content began with the answer, then "Let me verify: ... = 391"
-# followed by a bare "</think") and the reasoning field comes back empty. With
-# it, the chain of thought lands in `reasoning` — the vLLM field name the fleet
-# documents (llama.cpp endpoints use reasoning_content).
-FRANK_PRIMARY_REASONINGPARSER=glm47