The arena is budget - non-expert - KV reserve - graphs - overhead. With util
capped at 0.982 (never 1.00) the graph estimate is the only plan term this repo
can shrink: the fork charges an analytic 256 MiB + 52 MiB/graph = 0.55 GiB where
the engine's own post-capture line reports 0.31 GiB, so pricing it at
256 + 12/graph returns ~0.24 GiB to the expert arena. MNBT 1024 -> 2048 is
arena-free: the registry overhead is base + 0.22 GiB per 1024 ABOVE its nbt 4096
anchor, and the fitted 5.25 GiB was fitted AT 4096.
Frank's order, two messages: "extend the max context of frank-small to
200700 per individual stream without further quantization", then
"reduce it to 2 streams for a total of 401400-ish tokens, set ubatch to
1k", then "unquantize the KV if we have extra space".
CTX_SIZE 540672 -> 401408 with PARALLEL 3 -> 2 gives n_ctx_slot 200,704:
above the 200,700 the harness compacts at, so a full-length request
lands in one stream instead of a 400 exceed_context_size_error. The pool
must divide evenly across slots and stay 256-aligned for the FA path,
which is why it is 401,408 and not 401,400.
CACHE_TYPE_V q8_0 -> f16: the freed third slot is exactly what pays for
unquantized values (24.5 -> 32 KiB/token), and the budget lands back in
the 2-3 GiB band Frank set on 2026-10-02 instead of leaving ~6 GiB of
the card idle. The asymmetric-KV contract that created this endpoint is
spent with it; both --cache-type flags stay explicit.
--batch-size/--ubatch-size are now passed (2048/1024). The endpoint had
never passed either, so it ran on engine defaults nobody wrote down.
Franksdank-Runbook: serverAppConfigs-docs/LLM/frank-small-llama-mimo-q5km.md
Frank's order: replace frank-large with
kingjones777/Qwen3.8-27B-Uncensored-ROCmFP4-STRIX-MTP-GGUF's
Qwen3.8-27B-Uncensored-Q6_0_ROCMFPX_AGENT.gguf, following that card.
The file is ftype 114 — 339 GGML_TYPE_Q6_0_ROCMFPX (102) + 159
GGML_TYPE_Q8_0_ROCMFPX (103) + 353 F32 over 851 tensors,
qwen35/64 layers/full_attention_interval 4/4 KV heads/262,144 window —
read out of its own header. The pinned ROCmFPX build already knows both
types (ggml.h 102/103 plus convert/cpy/getrows/mmq/fattn device paths
for 102), so no rebuild: the fork stays at 36e9acd and the payload
directory is the only new host state.
Card compliance, all already our flags: Q4_0 draft head (not Q8_0),
--spec-draft-n-max 4 (the knee on both cards), --spec-draft-ngl 99,
--jinja, --fit off, -fa on, -dio, one slot at full window, thinking with
the card's own sampling triple (which the file also embeds as its
general.sampling defaults). Kept from the deployed shape: the BF16
projector (this repo ships only a Q8_0 one; BF16 is the file verified
4/4 on spatial ground truth here) and the byte-identical MTP head, which
the new script hardlinks instead of re-fetching 1.6 GiB.
Franksdank-Runbook: serverAppConfigs-docs/LLM/frank-large-rocmfpx-q6agent-uncensored-mtp.md
Four harness gaps that block an honest MTP re-measurement:
- measure: AR_TEMPS sweeps sampling temperatures (default "0,0.7"). Draft
ACCEPTANCE is sampling-dependent — a greedy draft matches a greedy target
exactly and is rejected more under a non-greedy target — so judging a spec
arm only at temperature 0 judges half the contract. The unsuffixed keys stay
temperature 0, so every historical number remains comparable.
- window: capture an evidence.log sidecar (expert-plan lines, compile path,
graph sizes, /metrics spec-decode and cache counters, OOM signatures). The
arena/overhead/reserve numbers exist ONLY in the boot log; an arm that loses
decode speed is otherwise unexplainable afterwards.
- orchestrator: FRANK_PRIMARY_AR_ENV_FILE stages an env other than the repo's,
which is how arms A/B without mutating the checkout (the fingerprint covers
whatever is staged, so the memo can never answer one arm with another's
numbers), plus METRIC lines for the swept temperatures and the evidence dump.
- scripts/autoresearch/frank-primary-arms.sh: runs a sequence of full windows
(baseline, MTP3+probabilistic with a step-4 capture ladder at precise memory
ON, the same at the default overhead, and the vendor SEQS-16 shape), gating
each staged env on its JSON knobs still parsing AFTER sourcing — an unquoted
{"temperature":0.7} loses its quotes at source time and reaches the engine as
{temperature:0.7}, which is a 20-minute boot wasted.
Three engine knobs that were impossible to set without editing the run line:
FRANK_PRIMARY_SPEC (--speculative-config), FRANK_PRIMARY_COMPCONFIG
(--compilation-config) and FRANK_PRIMARY_GEN_CONFIG
(--override-generation-config). Each holds the whole JSON value; empty or
unset means the flag is absent, so the shipped shape E argv is unchanged.
The seam re-pins the law that justified the omission: "speculative decode is
ABSENT BY MEASUREMENT" compared the wrong pair — c1248d4 retired MTP by
weighing fp8+MTP4 (37.4-40.6 t/s, 54% acceptance) against the bf16 no-spec
boot (36.4-39.4), bf16 then proved unbootable, and fp8 came back WITHOUT MTP.
fp8+spec was never measured against fp8+no-spec on one shape, the no-spec
incumbent runs ~30 t/s under the fleet's 35 t/s target, and every vendor
flash-next recipe ships MTP3.
Also in the seam: a dead assertion is deleted (a failing `! grep` mid-test is
exempt from bats' errexit, so the pre-precise-memory `! grep
--expert-precise-memory` never ran), and the knob test asserts by RENDERING the
flag from the sourced env instead of grepping for an assignment.
Live after the 1ac4d48 deploy: 'expert_precise_memory': True, runtime overhead
5.25 GiB, expert cache space 17.22 GiB / 7522 slots, swap region 13.7 GiB /
5986 slots, GPU KV cache 398,658 tokens / 1.99x, pool 3,150,000,000, MNBT 1024
-- the probe's projection is now production.
Compile cache proven on consecutive boots of that shape: cold 'Compiling a
graph for compile range (1, 1024) takes 179.24 s' / 'torch.compile took
188.26 s'; warm 'Directly load the compiled graph(s) ... from the cache, took
2.143 s' / 'torch.compile took 3.97 s in total' / 'init engine ... took 41.13 s
(compilation: 3.98 s)'. Host dir 1.4 GB / 4,707 files. Time to /health stays
payload-bound (490 s warm), so the claim is ~184 s of inductor/AOT build per
restart, not a proportional boot cut.
Tests: 367/367.
Measured by boot probe on the shape-D base (production stopped, candidate
booted AS the service), plan numbers read off the engine's own log:
runtime overhead 7.60 -> 5.25 GiB (precise gfx1201 TP2 registry)
expert cache 14.87 -> 17.22 GiB
slots 6,496 -> 7,522 (+1,026, +16%)
swap region 13.7 GiB / 5,986 slots
boot healthy in 420 s, no OOM
'--expert-precise-memory': True also appears in non-default args, so a silent
argparse drop is visible. The pool is NOT cut (option A) because the pool is
not an arena lever: 3.15e9/rank keeps shape D's measured 1.99x concurrency,
and a 2.2e9 arm probed in the same window (277,686 tokens, 1.38x) is recorded
as the conservative fallback.
Boot-cost bug found while diffing Frank's 3xR9700 recipe: the image ships no
/root/.cache at all, our boot writes 1.1 GB of vLLM AOT/torch_compile
artifacts (4,665 files) plus 229 MB of comgr JIT kernels into the ephemeral
layer, and podman rm -f discards them — so every deploy/restart/reboot re-pays
'torch.compile took 181.20 s in total' (173.19 s of it one graph). Mounting
FRANK_PRIMARY_CACHE_DIR at /root/.cache persists both and hides nothing.
GPU_MAX_HW_QUEUES=2 added at Frank's direction (real HSA knob); not A/B
measured. Retracted my own recipe-diff claim that cudagraph_capture_sizes
needed pinning: capture is 3 PIECEWISE + 2 FULL graphs, 3 s, 0.31 GiB.
Tests: 367/367.
`deploy.sh <target>` with no `--service` also walks the per-service revision
markers and restarts everything behind HEAD whose paths changed — a drain, not
a sync. That is how the latent plex gate bug surfaced: the backlog fired all
at once and plex's http:// readiness probe, which can never succeed against a
PMS that drops plain HTTP off-loopback, burned its full 600 s and aborted the
run with every later service left pending.
Both facts recorded: AGENTS.md gains the drain law under Deployment, log.md
gains the plex entry with the evidence chain.
deploy_plex gated on `curl http://127.0.0.1:32400/identity`, which can never
succeed: Plex accepts the TCP connection (it arrives on the container's eth0)
and then answers with nothing, so curl reports "Empty reply from server" (52).
Measured 2026-10-02 on a plex that was healthy the whole time —
`https://127.0.0.1:32400/identity` returned 200 in 44 ms while the gate burned
its full 600 s and failed the deploy.
The repo already knew this law in another place: tests/caddyfile.bats pins the
plex vhosts to a TLS upstream with the comment "PMS drops plain HTTP
off-loopback". The deployer's gate never got the same treatment, and plex's
per-service revision had been behind long enough that the gate had not run
against this posture until now.
-k because PMS serves its own self-signed cert on 32400.
index.md intro and mermaid nodes still described 4 x 262144 fp8/bf16 and
frank-small at 2 x 262144; the active-deployment list had no frank-primary
entry at all. Fixed both, plus a malformed GLM list item.
log.md gains the shape-D entry (the SEQS=2 constraint, the arena-lever
measurement, the 2x2 probe table, the measured 1.99x), and the frank-small
entry's stale consequence bullet now points at the resolution.
frank-small runbook: the compaction-fit note is corrected - the 200704 window
moved frank-primary's trigger to 140,492, which fits a 180,224 slot again.
Comments/docs only - no functional change. The live boot of c769ff2 reported:
GPU KV cache size: 398,658 tokens,
Maximum concurrency for 200,704 tokens per request: 1.99x
expert cache space 14.87 GiB / 6,496 slots of 2.344 MiB
The arena prediction (6,496) landed exactly; the concurrency prediction (2.04x)
did not, because the per-token KV price is SHAPE-DEPENDENT: 7,701 B/token at
MAXLEN 262144 (6.1e9 -> 792,069 tokens) but 7,892 at 200704 (3.146e9 ->
398,658). Never extrapolate a concurrency figure across geometries.
Also smoke-verified: three concurrent 67-token requests answered in parallel,
~4.1 s each - the 3 slots are real, and the pool admits 2 full-length.
Re-applies the desired shape incrementally on the proven-good fp8 3x262144
base, after bisecting why the first attempt (30904ba) crash-looped.
Root cause: `--max-num-seqs 2` cannot compile on this model. AOT
build_guards(strict_error=True) inside profile_run -> _dummy_run specializes
`query_start_loc`'s width to SEQS + 1 (3 at SEQS 2, 4 at SEQS 3) and the
ConstraintViolationError kills engine init ~7 min in. MNBT is orthogonal:
SEQS 2 failed at MNBT 1024 AND 2048; SEQS 3 passed at both. KV dtype was a
red herring. Measured by four single-variable boot probes.
Arena lever measured: the expert arena is sized from `max_model_len` via the
registry-priced KV reserve (9190 B/token x max_model_len), NOT from
--kv-cache-memory-bytes. 262144 -> 14.30 GiB / 6,248 slots; 200704 ->
14.87 GiB / 6,496 slots. The 6.1e9 -> 3.15e9 pool cut bought zero arena and
is a runtime-headroom lever.
Shape: MAXLEN 200704, SEQS 3 (unchanged - the only slot count that compiles),
KV_BYTES 3.15e9/rank = 2.04x full-length concurrency at 7,701 B/token fp8,
MNBT 1024, gather 32x64, no spec decode, no prefetch, no precise-memory.
The 2 x 200704 fp8 / MNBT 1024 shape (30904ba) crash-loops at boot: the
engine dies in determine_available_memory -> profile_run -> _dummy_run with
the SAME torch.compile dynamo constraint that killed the bf16 2 x 262144
shape on 2026-10-01:
RuntimeError: Worker failed with error 'Constraints violated
(L['query_start_loc'].size()[0])! You marked it as dynamic but your code
specialized it to be a constant (3)'
Not an OOM. Restoring the last shape measured serving (fp8 3 x 262144 /
KV 6.1e9 / MNBT 2048 / arena 6,248) so the endpoint serves again; the
desired edits are re-applied one at a time from this base.
Benchmark entrypoint: bash autoresearch.sh
Goal: Make frank-primary faster: raise decode tok/s on the tcclaviger vLLM endpoint (dankserver :7770, Qwen3.8-Flash-Next-Uncensored-FP8, 2x R9700, automatic tiered-expert offload). Context size may be altered (MAXLEN/SEQS/KV_BYTES and friends); quantization may NOT be altered (the 131-shard FP8 payload and the digest-pinned image are frozen).
Runbook, both indexes, AGENTS.md (topology row, endpoint section, three new
Key Lessons, one stale SGLang label in the bench list) and the bundle log now
describe the deployed shape: pool 540,672, 180,224 tokens per conversation,
f16 K + q8_0 V, mmproj vision, 21,684 used / 2,441 free of 24,576 MiB, 106.8
tok/s decode, 3,814 t/s prefill.
The claims that mattered:
- three contexts buy 180,224 tokens per conversation, not the native 262,144,
and a longer prompt is a hard 400 - so models.yml contextWindow is 180224
and frank-primary's 183,501-token compaction request no longer fits here
(forward pointer added to the retired SGLang runbook, which asserted it)
- the vision tower bills ~1.4 GiB for a 918 MB file, established by subtracting
two measured boots, not by a formula
- /v1/chat/completions ignores the legacy top-level 'images' field: the model
invents a color; only the image_url content part reaches it
Measured after the first 3-slot deploy: 3 x 196,608 booted (n_slots=3,
n_ctx_slot=196608, multimodal loaded) but used 22,972/24,576 MiB - 1,153 MiB
free, outside the 2-3 GiB band. The two measurements now recorded in
frank-small.env price the vision tower at ~1.4 GiB (non-KV 7,390 -> 8,859
MiB), so the pool drops 49,152 tokens (16,384/slot, 1,176 MiB) to land ~2.7
GiB free. The bats budget gate is recalibrated to the measured 9 GiB non-KV
cost, which now rejects the 3 x 196,608 shape instead of blessing it.
CTX_SIZE 589824 / PARALLEL 3 -> three independent 196,608-token conversations
(a mamba hybrid cannot share one KV cache, so per-slot window = pool/slots;
3 x 262,144 would need 18.4 GiB of KV alone and busts the card).
Vision comes back via bartowski's mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf
(918,166,048 B, sha256 ff348f31..., fetched + verified by
scripts/download-frank-small.sh): the Q5_K_M GGUF ships no v.* tensors, so
--mmproj IS the image path. MMPROJ_FILE without a colon default so an explicit
empty value is a real text-only opt-out, and the deployer now gates on the
projector exactly as the serve script does.
Budget is measured, not guessed: the 2 x 262,144 shape used 20,190/24,576 MiB,
which prices non-KV cost at 7,390 MiB. Three slots + projector land near
22.5 GiB, i.e. ~2 GiB spare - the bottom of the requested band, to be
confirmed by measurement and laddered if it lands outside 2-3 GiB.
Staged 59 GiB of retired LLM payloads on Dankclaw to
~dank/.AGENTTRASH/20261002-retired-llm-payloads/ (vLLM MiMo BF16, Qwen3.5-9B
MTP source, both DFlash dirs, Gemma4-12B-QAT, SGLang MiMo INT8) and corrected
every doc claim about what is on disk: gufo's rollback pair (Q4_K_XL weights +
the Q8_K_XL DFlash2 drafter) and frank-small's Q6_K stay in place, so the
rollback paths in the tags' env files still resolve. The vLLM-era frank-large
fetch trio moves to old/ with its runtime; runbook links updated.
frank-large: new runbook frank-large-rocmfpx-q8agent-mtp.md — the ftype-115
tensor-type lock-in that forced the fork, the pinned build (set_rows history),
the measured MTP A/B (17.0 vs 7.6 t/s decode, acceptance 0.462), the live
client contract (reasoning_content, effort fold, vision restored) and rollback.
frank-small: new runbook frank-small-llama-mimo-q5km.md; q6k marked retired
(rollback target). gufo runbook marked retired (rollback target). Both indexes,
the fleet topology table, the vision/models.yml notes, the benchmarking law and
three new Key Lessons updated.
The repo's highest-quality rung is ftype 115: 311 tensors of
GGML_TYPE_Q8_0_ROCMFPX, a type only the ROCmFPX fork reads, so the runtime
moves from gufo to scripts/build-rocmfpx.sh's pinned fork build (36e9acd) —
the first build on this host measured to load this file without the
2026-08-25 set_rows segfault. One session at the native 262,144 window, f16
KV (64 KiB/token, 16 GiB), the repo's Q4_0 MTP head drafting 4 tokens, plus
the bundled BF16 projector so the endpoint has vision again.
The serve script now honors caller environment overrides over the env file
(a plain source let the file win, which made single-run overrides no-ops) and
defaults apply only when a value is unset, so an explicit LLAMA_BIN='' is the
error it reads as.
One quant step down from Q6_K on the same CUDA build and the same shape:
2 x 262,144 with K f16 + V q8_0. The 0.86 GiB freed from the weights is
headroom, not a new window — the native 262,144 cap is already reached,
and f16 V would still cost 3.96 GiB more than the pool can carry.
Live battery green after deploy #3: alias, reasoning split into
`reasoning`, tool_calls, /metrics acceptance pos0 69% pos1 44% - the
cross-model MTP head performs as measured in the bench window.
The first crash-looping boot (51 restarts): serve received the host path
/home/dank/models/... but the models tree is mounted at /models inside the
container, so transformers failed 'Repo id must be in the form
namespace/repo_name' before the engine even built. Same conversion the spec
draft already used. Pinned by bats. Root docs index links the new runbook
(seam required it) and the work log records the crash-loop plus the 52 G
payload reclaim.
The 2026-10-01 first deploy refused /home/dank/models/Qwen3.5-9B because
the handler checked for a single model.safetensors (the dflash layout);
the Qwen3.5-9B MTP head ships 4 shards behind model.safetensors.index.json.
Same dual-layout acceptance as the serve script, now mirrored and gated by
the seam. The gate fired before the stop, so the live gufo unit was never
touched (health 200 throughout).
New runbook carries the roof math, the measured three-arm spec table, the
greedy-text numerics trap, the reasoning-field contract change and the
rollback tag. LLM index and AGENTS.md fleet table, bench policy and runbook
index follow; the frank-small index entries catch up to the SGLang cutover
(entry was missed on 2026-10-01). Work log records the cutover.
Engine: digest-pinned docker.io/kyuz0/vllm-therock-gfx1151 (vLLM 0.30,
TheRock ROCm 10, Triton GDN kernels); the serve script gates the image by
REPO DIGEST, the payload (config + index + exact shard count), the spec
draft (BOTH weight layouts: single model.safetensors for dflash, sharded
index for the Qwen3.5-9B MTP head), and whole /dev/dri, sweeps the bench
identity vllm-mimo, then exec podman wait so a container death trips
Restart=always. Shape: 4 x 262144 at util 0.45 (KV grows only on the 8
full-attention layers), MTP 2 draft tokens.
Measured 2026-10-01 window (frank-large stopped): base 13.1 t/s decode
(~94% of the LPDDR5x BF16 roof), DFlash-8 17.4, MTP-2 19.8 with 71%/44%
per-position acceptance - the cross-model head works because vLLM reads the
draft from a separate path while MiMo ships zero mtp.* tensors. Spec arms
do not preserve greedy bf16 text (batched-verify reduction order).
The seam was rewritten around the new laws (18 tests): unit self-healing,
digest gating, engine wiring, the measured spec table, the retired gufo
gates, the deployer MainPID check. frank_small port test follows the
PORT -> FRANK_LARGE_PUBLISH rename. Rollback: tag frank-large-gufo-q4kxl-20261001.
All three verify every file by byte count + Hub LFS sha256, use -f (a bare
curl -o reports success on a 403 error body), support --verify-only, and
never put a token on the command line. Payloads are on Dankclaw and verified
against these manifests.
bf16 at 2 x 262144 fails with and without --expert-precise-memory. The real
error (captured from the journal this time, container fixed via journald
rather than podman logs):
RuntimeError: Worker failed with error 'Constraints violated
(L['query_start_loc'].size()[0])! You marked it as dynamic but your code
specialized it to be a constant (3)'
- a torch.compile/dynamo dynamic-shape violation, NOT an OOM. Ruled out a
stale compile cache: ~/.cache/vllm holds only modelinfos (16K) and each
podman run gets a fresh container filesystem.
The deltas from the last shape measured serving (fp8 3 x 262144 / MNBT 2048 /
792,069 tokens / 3.02x / arena 6,248) are KV dtype (fp8 -> bf16) and SEQS
(3 -> 2). Restoring the known-good shape; bisect bf16 vs SEQS in a window,
with --enforce-eager as the bypass.
NRestarts climbed to 3, each attempt ~6.7 min then 'Deactivated successfully'
(API server drains after an EngineCore fatal error; Restart=always turned the
silent death into a visible loop). The arena is device-resident, so the budget
decides it: weights 6.28 + pool 6.80 (bf16 2 x 262144) + graphs 0.55 + arena
16.65 (precise overhead 5.25) + non-torch 1.45 = 31.73 of 31.86 GiB -> ~0.13
GiB free at boot, and graph capture is what allocates transiently.
Dropping the flag restores the 7.60 GiB default overhead: arena 14.31 GiB,
total ~29.4 GiB, ~2.4 GiB free. bf16 2 x 262144 (Frank's KV change) stays.
The serve script keeps the opt-in flag; the seam now asserts it is ABSENT.
Live-verified post-deploy: context_len=262144, max_running_requests=2,
max_total_num_tokens=606208 in the boot log; a 200,000-token prompt
(compaction height is 183,501) prefilled+completed in one request
(97.3 s, ~2,056 tok/s eager); two concurrent 50k requests answered.
Docs-only change.
Frank's call. Payload: ajgazin's abliterated Swift-1.5 UD-Q8_K_XL (29.3 GiB,
SWIFT OPEN LICENSE) -> unsloth/Qwen3.8-27B-GGUF UD-Q4_K_XL (16.35 GiB,
sha256 3f227079...b01e, vanilla/Apache-2.0). NOTE: the endpoint is no longer
uncensored — that was one of the reasons for the Swift payload on 2026-09-28.
New fetcher scripts/download-frank-large-q4.sh (no auth header: repo is public
and ungated; size + sha256 verified after transfer). The speculative drafter
and its fetcher are unchanged.
Shape: --sessions 4 --context 163840 with unquantized KV. gufo exposes no KV
quantization lever (execution_policy.hpp pins kv_cache_storage to kFp16), so
f16 is inherent.
KV arithmetic: f16 KV = 64 KiB/token (16 full-attention layers x 4 KV heads x
256 head_dim x 2 x 2B; the other 48 layers are linear-attention with a
per-sequence fp32 state). 4 x 163840 = 655,360 tokens = 40.0 GiB KV, each
session exactly 10 GiB. Budget ~62 GiB of the 114 GiB GTT.
Seam retargeted (FETCH -> the q4 script, TARGET_STUB, context/sessions pins).
The KV pool stays pinned at 606,208 tokens (card is full: 531 MiB free at
the old shape; autofit's 687,388 would leave 0.53 GB and OOM on the first
long request). 2 x 262,144 = 524,288 leaves ~82k tokens of pool slack.
Motivation: harness compaction. frank-primary's contextWindow 262,144
compacts at 70% = 183,501 tokens, and the compaction request carries a
32,768-token summary budget -> 216,269 must fit ONE frank-small request.
The 151,552 slot window could not hold it; the native window does.
No spec-decode interaction: checkpoint ships zero mtp.* tensors (config
lies), seam keeps every --speculative-* flag away from the engine.
Both of Frank's changes at once.
KV is bf16 (--kv-cache-dtype auto) at two full-size windows (2 x 262144).
bf16 measures 13,839 B/token (bf16 3x241664 boot: 11,047,010,304 B -> 798,280
tokens), decomposing as 12,276 attention + 1,563 mamba/side caches vs fp8's
6,138 + 1,563 = 7,701. 524,288 tokens needs 7.256e9 B; pinned at 7.3e9
(~527,500 tokens, 3,206 margin). Pool cost: 5.68 -> 6.76 GiB per rank.
--expert-precise-memory selects the fitted overhead (5.25 GiB) over the default
(7.60 GiB = fitted + compile/graph-capture headroom). It moves only the plan's
term, so the expert arena goes 6,248 -> ~7,278 slots (~1,025 more) at the
cost of boot-time capture slack. Overhead adds 0.22 GiB/1024 nbt above 4096, so
MNBT 2048 pays nothing.
Seam + docs + AGENTS row + log updated.
The gate failed twice in the same spec for two different reasons and then passed
33/33 on a manual re-run. Future sessions should re-run before treating the
DEPLOY INCOMPLETE banner as a real fleet fault.
ExecStop runs 'podman stop -t 120' but the unit never set TimeoutStopSec, so
systemd's 90 s default cut the graceful stop short: journal shows 'Stopping
timed out. Terminating.' then 'Failed with result timeout' on 2026-10-01
13:27:17 — every stop of this unit was logging a failed stop. 180 s covers the
engine's drain with margin, and the seam now guards the RELATIONSHIP
(TimeoutStopSec > the podman grace) rather than a literal.
StartLimitIntervalSec is a [Unit] directive; the copy in [Service] made systemd
log 'Unknown key StartLimitIntervalSec in section Service' on every load. Seam
asserts it appears exactly once and not in [Service].
Frank's call 2026-10-01: MAXLEN 262144 x SEQS 3 = 786,432 tokens of contract.
Measured fp8 KV costs 7,698 B/token (4-slot boot 8,311,013,376 B -> 1,079,582
tokens; 5-slot boot 10,388,766,720 B -> 1,350,182 tokens), so the pool floor
for the contract is 6.054e9 B. Pinned at 6,100,000,000 (~792k tokens, ~5.9k
margin) so the remaining ~2.2 GiB versus the 4-slot shape goes to the expert
ARENA - the fork sizes its decode-critical expert cache from the remainder
after weights, pool and workspace.
MNBT 2048 (Frank): the arena sweep measured 6,499 slots at every rung <= 4096,
so the larger batch costs the arena nothing.
Seam retargeted (3 seqs / 6.1e9 / MNBT 2048); docs + AGENTS row + log updated,
including the closed DCP investigation.
--decode-context-parallel-size 2 fails VllmConfig validation at boot:
'Decode context parallelism for GQA/MQA requires --tensor-parallel-size (2) to
be greater than the model's total number of KV heads (2)'. DCP shards KV along
the sequence dimension AFTER KV heads spread across TP ranks, so it needs
TP > num_kv_heads; this model has 2 KV heads, so TP >= 4 — four GPUs, not two.
Recorded with the rest of the survey (PP/DP/EP/multi-node verdicts for this
box), the container-injection requirement for /home/dank/mtp_measure.py
(it imports /bench_iter.py by absolute path and must be copied in), and the
re-measured deployed capacity (1,079,582 tokens / 4.12x).
DCP is passed only when FRANK_PRIMARY_DCP is set, so the deployed shape is
unchanged. Verified in this fork: DCP reuses the TP ranks when PCP is
disabled (tp % dcp == 0), so TP=2 + DCP=2 fits the two R9700s with no extra
ranks; the flash_attn backend carries DCP support, the 36 linear-attention
layers of this hybrid do not — which is what the A/B window tests.
Replaces llama.cpp Gemma4-12B-QAT (payload + binary retained as rollback).
SGLang in a self-contained python3.12 venv with an extracted CUDA 13.0
nvcc toolchain for the sgl-kernel/flashinfer JIT.
Shape: fp8 KV, pinned 606,208-token pool / 4 x 151,552 slots, bf16 mamba
states pinned at 32 slots (the engine autofills the ssm pool to a fixed
~5.1 GB regardless of dtype), eager prefill (no headroom for graph
capture), no spec decode (the checkpoint ships no mtp.* tensors despite
mtp_num_hidden_layers=1 — NEXTN drafts with an uninitialised 4.24 GiB
ghost at 0% acceptance).
Verified: 83.9 tok/s decode, 170k single prompt, 4 concurrent 54k
requests, vision (in-checkpoint tower), reasoning_content field.
Five 262144-token contexts passed every boot gate (KV 1,350,182 / 5.15x) but
left 0 bytes free at runtime: a real client request (995-token prompt,
max_tokens=261149, sampling params straight from models.yml) killed Worker_TP1
with torch.OutOfMemoryError 'Tried to allocate 50.00 MiB ... 0 bytes free'.
Boot is not proof of runtime survivability.
Four slots x 262144 fp8 (KV_BYTES 8,311,013,376/rank) returns ~1.93 GiB per
rank for activations/graph capture.
Unit: Restart=always instead of on-failure — a runtime worker fault makes
vLLM's API server exit clean, so ExecStart returned 0 and on-failure never
fired, leaving the endpoint down until a human noticed. Also
StartLimitIntervalSec=0 and OOMScoreAdjust=-500.
Frank's call after the night ladder: five contexts at the model's full native
262,144 window beat six shortened ones. Six full-length contexts do NOT fit at
any KV dtype (6 x 241664 fp8 asked 10.70 GiB against 9.18 GiB free — the
6-seq graph/activation footprint leaves too little for the pool); 5 x 262144
at fp8 PASSED on the first rung: KV_BYTES 10,388,766,720/rank, measured
'GPU KV cache size: 1,350,182 tokens, Maximum concurrency for 262,144 tokens
per request: 5.15x' (39,462 tokens of margin).
Also documented: the SEQS=3 bf16 max (241,664, KV 732,359 / 3.03x) measured
earlier the same night, and the root-cause read that the 7.2 GiB KV ceiling
law was the MTP4 workspace, not VRAM. Seam and harness (contextWindow 262144)
re-pinned.
Night ladder result (Frank's subtract-16k method): 262144 and 245760 both OOM
at CUDA graph capture ('Tried to allocate 76.00 MiB ... 0 bytes free'), 237568
passed (KV 718,606 tokens), and the +4096 bonus step PASSED -> 241664 is max
possible and DEPLOYED at util 0.98 (KV 732,359 tokens / 3.03x concurrency at
241,664-token requests). Util stays 0.98: lowering it shrinks the engine's own
budget instead of buying slack.
Client contract restored (the GLM rollback d91379d dropped all three and the
qwen rollback inherited the gap): --reasoning-parser qwen3 fixes the malformed
thinking block (thinking was landing inline in content with a dangling
</think> and a null reasoning field), --enable-auto-tool-choice with
--tool-call-parser qwen3_xml makes tools work, and
--default-chat-template-kwargs enables enable_thinking + preserve_thinking.
Harness: contextWindow 241664 (compaction gap recomputed), preserve_thinking
moved into chat_template_kwargs.
The 0.98 boot died at KV allocation: torch OOM tried 10.28 GiB (the exact
3x262144 bf16 pool = 11,047,010,304 bytes) with 9.46 GiB free per rank.
The rootless bench probe had booted the same numbers; the root unit's
pinned-expert path holds ~0.8 GiB/rank more. Util 0.90 returns ~2.5 GiB/rank
of workspace with the pool ask unchanged; measured KV capacity law
(798,280 tokens at 11.05e9) unchanged.
Overnight redo campaign verdict + Frank's call: the fp8+MTP4 shape is retired
to git tag frank-primary-fp8-mtp-20261001. The new incumbent: KV cache
unquantized (auto), no speculative decode, KV_BYTES 11,047,010,304/rank
(engine-measured 798,280 tokens, 3.05x concurrency at 262,144-token
requests), MNBT 1024, MoE gather 32x64 (+18.7% decode, grows with depth).
Measured at the deployed shape: 39.38/38.79/36.42 tok/s (166/1087/4064 ctx).
Seam re-pinned: the 7.2 GiB/rank KV ceiling was the MTP4 workspace, not VRAM;
spec decode absent by measurement; bf16 trade documented (clean cache +
262k windows for ~14% decode at 4064 ctx).
- AGENTS.md: fleet table + frank-primary section back to qwen FP8 + MTP4
(37.4-40.6 tok/s, 54% acceptance); GLM runbook moved to the retired index.
- runbook: RETIRED verdict — DFlash2 drafter served and drafted but
acceptance pinned 1.00-1.38 on every text class including verbatim
repetition (mis-wired aux capture, not a weak drafter); 5x slower than
baseline at ~1.2 acceptance.
- log.md: 2026-10-01 rollback entry.
- deploy-remote.sh: LLM_PRIMARY_CONTAINER back to frank-primary-qwen;
glm-probe joins the legacy sweep list.
- frank_primary_unit.bats: re-pinned to the qwen shape — KV qualification
law (7.2 GiB/rank, 3.65x concurrency; 9/16 GiB OOM), fp8 q8/q8 KV
PRESENT, MTP4 PRESENT (3.1-3.4x over the 12.11 no-spec baseline), no tool
dialect, automatic offload. The old 'MTP buys zero' law carried the
prefetch tax and is retired.
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.
use_aux_hidden_state_outputs lives on the gpu_model_runner instance
(gpu_model_runner.py:623/678), not on the spec config, so the first
registry check never fired and the reserve stayed at 13,600 B/token.
Switch to method in {dflash, dspark, extract_hidden_states}.
The dflash boot cleared the unifier and the kpool tail (02/03) and died on
KV capacity: on the generic path the mamba states are NOT aliased into the
MLA pages (the glm5 tensor layout's whole point), so the capacity estimator
demanded ~30.6 GiB fixed against 0.92 GiB available (the plan's KV reserve
registry only knows the target's 13,600 B/token).
Patch 04 keeps the aliased glm5 pool and appends the drafter's uniform
group and per-layer hidden-state groups beside it: grouping partitions
hidden + draft specs, the layout returns a 10-tuple, the builder sizes one
shared backing allocation (pool + extras), and the memory estimator adds
the extras. Patch 04b extends the GLM registry kv_bytes_per_token by the
drafter (4,096 B/token) and hidden-state (5 x hidden x 2 B/token) costs so
the expert plan reserves enough.
Local smoke tests (CPU torch + real spec classes) validate grouping shape,
layout recognition, builder block math and memory accounting against
synthetic GLM+draft+hidden spec sets. Apply-verified: reproduces shas
5521e50b... / 1a623d5c...
Patch 02 (MLA-multiple max page) cleared the KV page-size unifier: the
dflash boot now reaches cudagraph capture. New blocker: the kpool decode
kernel asserts tail_kv_cache.shape[2] == pool_size; KpoolTailSpec pins
block_size == index_kpool, so the unifier's divisible branch must never
grow it. Patch 03 = 02 + a KpoolTailSpec branch that pads page_size_padded
instead. Apply-verified: reproduces patched sha 5093e4b7... Runbook
updated with the full KV grouping journey.
Runbook: the five-version evolution recorded, each trap named (method
hand-write, Protocol-base MRO kill, wrong-wrapper attrs). The decisive
discovery: the runner checks the LOADED instance, which for GLM-5.3-Flash
is the multimodal Glm5NextForConditionalGeneration holding the text model
as self.language_model — v4's attrs on Glm5NextForCausalLM were invisible
to it. v5 (attrs + methods on both wrappers) passes: both TP workers log
'Using Eagle3 auxiliary layers from config: (6, 15, 25, 34, ...)' and the
DFlash2 drafter resolves + loads. Log: the DFlash bullet updated from
'the trunk simply does not implement' to 'gap closed; serving end state
still unproven (window RAM contention interrupted the boot twice)'.
The v4 boot named the actual miss: model_runner.py:425 calls
set_eagle3_aux_hidden_state_layers(self.model) on the instance model_runner
LOADED -- and for GLM-5.3-Flash that is Glm5NextForConditionalGeneration (the
multimodal wrapper; APIServer log: 'Resolved architecture:
Glm5NextForConditionalGeneration'), which wraps the text model as
self.language_model = Glm5NextForCausalLM (glm5next/common/model.py:1113).
v4 put the three presence-checked attributes only on Glm5NextForCausalLM,
so the isinstance check on the conditional still failed with the same
RuntimeError.
v5 adds to BOTH wrappers, MRO-safely (attribute route, no Protocol base):
- Glm5NextForConditionalGeneration: supports_eagle3 / has_own_lm_head /
has_own_embed_tokens + the two methods delegating to self.language_model
(the pixtral.py:498 pattern);
- Glm5NextForCausalLM: the same three attributes + the two methods that
unwrap to self.model (the EagleModelMixin trunk -- v1's correct bodies).
Also proven by MRO simulation: the ForCausalLM's five-base ordering cannot
take SupportsEagle3 as a base in ANY position, so inheritance was never a
viable route for this model.
base 008eeaa2... -> patched bc9913a1..., apply-verified against the pinned
image (patch -p1 reproduces the sha256 and matches the mounted file).
The v4 window boot named the real presence-check rule: isinstance against a
runtime_checkable Protocol walks __protocol_attrs__ (which includes members
inherited from BASE protocols) and getattr_static()s each one off the instance,
breaking on the first missing attribute. SupportsEagleBase declares
has_own_lm_head/has_own_embed_tokens as plain instance-var annotations, so
v3's bare supports_eagle3 flag still failed. Replicated in-image with stock
typing: flag-only class -> False; flag + the two base members -> True.
v4 declares all three on Glm5NextForCausalLM:
supports_eagle3: ClassVar[Literal[True]] = True
has_own_lm_head: bool = False
has_own_embed_tokens: bool = False
(False is correct for the two: the wrapper delegates embed/lm_head to its
trunk. The MRO-safe attribute path is kept from v3.)
base 008eeaa2... -> patched 60f27aad..., apply-verified against the pinned
image (patch -p1 reproduces the sha256 and matches the mounted file).
v2 (inheriting SupportsEagle3 as a base) taught the real lesson: the isinstance
check PASSES and the class then dies at creation with
'Cannot create a consistent method resolution order (MRO) for bases ...'
-- GLM's outer wrapper already inherits vLLM Protocols (SupportsPP,
MixtureOfExperts, HasInnerState, IsHybrid) whose ordering is incompatible with
appending another Protocol base.
v3 avoids the MRO entirely: declare
supports_eagle3: ClassVar[Literal[True]] = True
as a class attribute on Glm5NextForCausalLM. Runtime-checkable Protocols do
presence-based isinstance, so the bare flag satisfies supports_eagle3(model)
without touching the MRO. The aux-layer plumbing stays on the trunk via
EagleModelMixin.
base 008eeaa2... -> patched 4c9a632b..., apply-verified against the pinned
image (patch -p1 reproduces the sha256 and matches the mounted dflash-patch
file byte for byte).
v1 hand-wrote set_aux_hidden_state_layers/get_eagle3_default_aux_hidden_state_layers
and the boot reproduced the identical 'Model does not support EAGLE3 interface'.
Reason: supports_eagle3() is an isinstance check against a runtime-checkable
Protocol whose members include a required ClassVar flag
(interfaces.py:1663 supports_eagle3: ClassVar[Literal[True]] = True), so methods
alone do not satisfy it. Inheriting SupportsEagle3 -- the idiom every model in
this image uses, including the fork's own DFlashLagunaForCausalLM -- supplies the
flag, SupportsEagleBase's has_own_lm_head/has_own_embed_tokens, and working
default implementations of both methods; the hand-written methods are deleted.
base 008eeaa2... -> patched 5eea89c5..., apply-verified against the pinned image.
Runbook: corrected method name, recorded the Protocol trap as the generalizable
lesson for future spec-decode ports.
Two operational facts the cutover surfaced: PYTORCH_CUDA_ALLOC_CONF=
expandable_segments:True is part of the qualified shape (the plan fills VRAM to
its modelled headroom, so the default allocator's fragmentation fails
profile/graph capture — measured on the deployed unit), and the deploy's e2e
gate drives the admin open-links spec, which walks every guarded vhost
including 192.168.0.112:8989 on FrankBook-Pro — a sleeping Mac mini fails that
spec and reports DEPLOY INCOMPLETE while the engine is healthy.
The deployed unit failed its first boot with the engine core OOMing during
profile/graph capture: "Tried to allocate 662.00 MiB. GPU 0 has a total
capacity of 31.86 GiB of which 240.00 MiB is free ... try setting
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True". The expert-offload plan
fills VRAM to its modelled headroom by design, so the default allocator's
fragmentation leaves the capture phase nothing — every successful bring-up
probe (both scratch-port and 7770) carried this env, and the repo recipe
dropped it.
Also pins the seam: the setting is part of the qualified shape, not an
optional tuning.
Payload: Intel/GLM-5.3-Flash-W4A16-AutoRound (AutoRound symmetric int4 group
128, auto_round:auto_gptq, 34 shards + 2 extras, 113,074 tensors, ~170 GiB).
It is the only W4A16 checkpoint this fork can serve with expert offload: its
quant_method "auto-round" is overridden to "inc", which routes the MoE to
libr4d's grouped W4A16 GEMM (R4dW4a16MoEMethod). A compressed-tensors W4A16
build of the same model resolves to TritonWNA16Experts, which has no offload
recipe, and refuses to serve at all (170 GiB of experts cannot fit 64 GiB of
VRAM, so offload is mandatory).
Shape: tcclaviger RDNA4 vLLM fork (same digest-pinned image as the 2026-09-29
qwen cutover), TP=2, 131,072 ctx x 2 seqs, bf16 KV (fp8 is ILLEGAL here — it
disables the sparse MLA backend's rope-free Triton path, the only MLA decode
gfx12 has), util 0.98, NBATCH 2048, --enable-expert-offload
--expert-precise-memory, prefix caching, glm47 tool + reasoning parsers,
MTP and expert prefetch absent by measurement.
Measured: decode 9.9-10.0 tok/s, prefill 918 tok/s (warm), arena 14.51 GiB =
2,383 slots/rank over 12,096 host-backed experts (73.64 GiB pinned/rank).
THE 35 tok/s TARGET IS NOT MET and the shortfall is measured: live telemetry
(R4D_LRU_TELEMETRY=1) gives a 0.53-0.66 per-layer hit rate, a step moves
0.82 GiB of misses in ~100 ms = 8.2 GB/s effective against a link measured at
22.74 GB/s — the same structural gather-path gap the qwen campaign found.
Growing the arena 13.08 -> 14.51 GiB changed decode by nothing.
Rejected by measurement: MTP4 (head loads, acceptance 3.76-4.90, but decode
unchanged and prefill collapses 918 -> 255 — a verification forward gathers the
union of the batch's experts), NBATCH 8192 (identical prefill).
Also: scripts/patches/gpu_worker-kv-cap.patch (fork-local fix for a KV-reserve
model that under-counts GLM's per-sequence KDA state; not needed by this shape),
runbook, AGENTS.md row + section, both indexes, work log.
Rollback: 2339fae (qwen FP8 + MTP4).
The classic 'podman images | grep -q' pattern races under set -o pipefail:
grep exits on its first match, podman dies SIGPIPE (141) writing the
remaining RepoDigests lines, and pipefail promotes 141 to the pipeline
status — the gate reads 'no match' and refuses to deploy (2026-09-30,
dankserver). Same bug in the serve script (it would restart-loop the
same way). Fix: capture podman's output into a variable first, grep the
variable. Patched in the R9V September deploys by luck of timing.
Unquoted value + set -a sourcing parses 'qwen-fp8' as a command ->
ExecStart exit 127 -> unit restart loop (caught on the first deploy).
The deployer's wait_for_health was killed and will re-run on the fresh
deploy.
Replace the R9V radiance fork with the tcclaviger RDNA4 vLLM stack (the
digest-pinned image the 2026-09-29 tuning campaign measured), serving
orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8 (131 shards, 172.8 GiB,
already on disk) with MTP4 spec decode.
Qualified by boot probe on 2026-09-29:
- MAXLEN=200000 x SEQS=3 (600k total) at --kv-cache-dtype fp8 (q8/q8 KV):
'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: global 16G and 9G both OOM (worker tried to
allocate the full size; ~7.5 GiB/rank is the ceiling with the arena).
- prefetch OFF by law (telemetry: 36% precision, ~50% wasted traffic);
TunableOp off (corrupts numerics).
- decode at this shape: 42.94/34.55/36.09 tok/s; arena 6,032 slots.
Trio: frank-primary.env (all FRANK_PRIMARY_* keys), serve-frank-primary.sh
(direct podman run; entrypoint execs vllm serve; waits on the container),
frank-primary.service (root unit, same ExecStop law, new container name
frank-primary-qwen). Deployer gates rewritten (image digest + shard count
+ weight-map index). download-frank-primary.sh is now inventory/verify
(repo is GATED — 401 anonymous, masked LFS hashes; payload already on
disk). Seam rewritten for the new contract. Pre-cutover tag:
frank-primary-r9v-q8-20260929.
PORTMAP=7773:7770 hook; health 200 on 127.0.0.1 and 192.168.0.235,
decode through the published path in band. 7773 chosen over 7770
(frank-primary owns 7770 when it returns). Log bullet recovered from a
misplaced insert into the 2026-09-28 cleanup section.
Repo tag qwen-fp8-tuned-20260929 at f532e03; serve script launches the
tcclaviger image by digest sha256:da402966... (local alias
qwen-fp8-runtime:tuned-20260929) — the floating :latest is no longer
referenced.
Source reading in the qwen-fp8 container: glm5next is registered with an
amd/ backend dir + DSA indexer; r4d.so has native moe_dsfp4/moe_dsfp4_mt1
entries (has_dsfp4()==True), so the old 'MXFP4 = emulation, missing
kernels' verdict is dead for this fork. DSFP4 = MXFP4 weights + DeepSeek
activation law. Offload recipes exist for mxfp4 and W4A16 (R4dW4a16MoEExperts
in offloader/expert.py:114). GLM gate is now the payload format
(compressed-tensors MXFP4/W4A16, none public) + a HIP boot probe.
The KV trim is inert: arena identical (5,648 slots) at 4.5 and 4.03 GiB
(planner buckets; the trim went to margin). Final 256k config
MAXLEN=262144 KV_BYTES=4328521728 prefetch off; full-speed alternative
MAXLEN=184224 at 3 GiB.
Asked for iterative decode/prefill improvement at a 32k working point
without touching the model or the quantization. There isn't one: gufo's
served settings are already at this engine's tuned point, so no config
change was made and none is proposed.
Measured on the repo's own prose and shell sources at 32,762 prompt
tokens, salted per run against a warm prefix cache, 3-5 repetitions:
* --prefill-chunk 512 -> 8192 moves prefill 410.7-412.4 t/s. A 16x change
in chunk size, 0.4% out. The lever is dead at this depth.
* --draft-tokens 5 -> 10 gives 13.79-15.86 t/s decode, all inside the
run-to-run noise. The 5-rep confirmation REVERSED the 3-rep leaders:
dt9+chunk4096 looked best and came last (12.86), the deployed config
came first (14.71).
* --draft-policy fixed vs adaptive: no effect. --min-draft-tokens:
rejected by the engine ("DFlash2 requires ... 1"). MTP: unsupported for
Q8 targets (token_embd.weight is Q8_0).
* --context 262144 -> 40960, tested as a diagnostic: no gain, and it
would break models.yml's declared contextWindow. Not shipped.
What does move the number is speculation itself — 6.65 t/s off vs ~15 t/s
on, 5.3x — and it is already enabled.
The two traps that cost most of the wall time are now in AGENTS.md's
benchmarking policy, because both produced FALSE results:
1. gufo bench cannot rank speculative settings and inverted the ranking.
Its corpus reports acceptance=0.933 +/- 0.000 with an identical trace
sha256 across draft widths — the same highly-predictable text every
run. It reported 29-37 t/s where real traffic gives ~15, and made
dt16 look 6% faster than dt7 when real text made it 14% slower.
2. A live A/B needs >=128 generated tokens. At 8, inter_token is
first-token noise; the first pass produced a nonsense ordering that
the 128-token pass reversed.
Plus the Server-Timing header is `ttft;dur=402.485` — the unit rides in
the NAME half, so splitting on "=" keys the dict "ttft;dur" and every
lookup silently returns nothing. Four configs reported "no Server-Timing"
while the header was present on all of them.
The disk was at 93% with 137G free. Nine retired payloads are now moved
to ~/.AGENTTRASH/20260928-retired-models/ with a MANIFEST.md giving a
restore command per item — moved, not deleted, and nothing is freed until
the purge is approved. Every one maps to a runtime already retired in the
runbook index.
Only the .gguf was taken from Qwen3.8-27B-UD-Q8_K_XL/: the live
frank-large DFlash2 drafter lives in that directory and stays put. The
manifest warns against restoring the directory wholesale.
Kept: checkpoints/ (23G) — unreferenced by the repo, but ComfyUI is live
and its workflows were not searched, so it is not confirmed dead. Also
logs/, because x4e-run.log is a SPARSE file (3.3G apparent, 4K real) and
frees nothing.
The reference check caught a live bug: serve-frank-small.sh's fallback
MODEL_DIR still pointed at the retired GSQ-RCO payload frank-small served
on 2026-09-16/17. frank-small.env has always overridden it, so the drift
was invisible — but a missing or unreadable env file would have turned a
config problem into a "missing asset" naming a model that is no longer
there. Repointed at Gemma4-12B-QAT/, which the env file already sets.
Not taken, by request: podman images (279G reclaimable, 95%) and the
uv+pip caches (73G). Together 352G of regenerable garbage, larger than
what was reclaimed here.
The 29.3G payload rollback target went with the rest, so the payload-only
rollback is now a re-fetch first; the runbook and AGENTS.md say so.
The target moves from unsloth/Qwen3.8-27B-GGUF:UD-Q8_K_XL to
ajgazin/Swift-1.5-Qwen3.8-27B-Uncensored-Dynamic-MTP-GGUF's UD-Q8_K_XL —
an abliterated fine-tune of the same base, under the non-OSI SWIFT OPEN
LICENCE 1.0. Port, alias, context, drafter and client contract are
unchanged.
Compatibility was settled from the GGUF headers before fetching anything:
both files parse to the same architecture (qwen35), the same 866 tensors
with identical names in identical order, zero per-tensor ggml_type or dim
mismatches, and the same MTP head. Two metadata differences that look
alarming are no-ops, confirmed against gufo's source — general.file_type is
deliberately not consulted (the file gufo already served records Q4_K_M
while containing no Q4_K tensor), and Swift's extra
qwen35.attention.recurrent_layers key is never read; layer kinds come from
full_attention_interval, which both files carry as 4 and which agrees.
The load test agreed: 9.2 s to listening, 66.5 GiB of the 116.7 GiB iGPU,
no loader warnings, and a green contract battery including a two-turn tool
loop.
The DFlash2 drafter did not move, so the payload is now two files in two
directories: DRAFTER_DIR is its own tunable. That broke the deployer's
stray-run sweep, which keyed on the target filename and would have stopped
matching — a stray run would then have held 7770 past the port gate. It
now keys on the drafter, which every frank-large command line carries.
The fetch script sends no auth header: the repo is public, and a token on
the curl command line is world-readable via /proc/<pid>/cmdline. The
anonymous transfer ran at 1.8 MiB/s; the host token through a mode-600
curl -K config reached 7 MiB/s.
Test seam: the mutation pass found four real gaps. Two fetch assertions
were satisfied by a header comment rather than the URL assignment, so
repointing a repository left them green, and nothing bound the env's
MODEL_FILE/DRAFTER_FILE to the filenames the fetch scripts download. Both
are now explicit. A harness bug worth recording: a bats --filter that
matches no test reports zero "not ok", indistinguishable from a mutation
that survived — the harness now checks the 1..N count. 16/16 caught.
The ad-hoc status commands kept producing wrong readings, most seriously
reporting "MemAvailable 0.0 GiB" on a host that actually had 240 GiB free.
Cause: /proc/meminfo separates key from value with a TAB, so
grep MemAvailable /proc/meminfo | tr -s " " | cut -d" " -f2
returns an empty field and awk prints 0.0. A false "host is out of memory"
reading is worse than no reading, because it invites an unnecessary abort.
dankstatus.sh parses on a colon split (tab-agnostic), cross-checks
MemAvailable against MemTotal before printing either, and refuses to print
a zero value. It also pins the rocm-smi CSV column order (device, total,
used) which was previously read swapped, and takes only the first line of
systemctl is-active/is-enabled, which emit more than one line here.
Incidental finding while validating: rocm-smi reports card2 (the 7700X iGPU)
as 0.50 GiB total, not the 123.0 GiB torch.cuda reports. torch was exposing
the GTT aperture. The device is smaller than documented, not larger.
Tests: 346/346 pass (no test surface touched).
Public, ungated repo so no auth header is sent — a token on the curl
command line is world-readable via /proc/<pid>/cmdline. Size and sha256
are the Hub LFS values (31457990272 / 9076f99a…), verified after fetch.
Not wired into frank-large.env: the gufo load test has to pass first.
Live numbers from the 2026-09-28 deploy: 13 s load to listening, RSS
30.6 GiB, ttft 549.8 ms / inter-token 24.2 ms on a short prompt, and a
green contract battery (health, ready, /v1/models at 262,144, content +
reasoning_content, tool_calls, SSE, and a two-turn tool loop).
Also recorded: gufo accepts every request field the harness sends in
extraBody and the full reasoning_effort set, both verified 200 — which
is why models.yml's reasoningEffortMap fold is now optional rather than
required, and why it is being kept anyway.
The .AGENTTRASH archive is ignored and exists only in a primary checkout,
so a filesystem grep fails there while passing in a worktree. git grep
searches the tracked tree, which is what the assertion is about.
gufo is a vertical inference engine built for exactly this hardware
(Ryzen AI MAX+ 395 / Radeon 8060S, gfx1151), and Dankclaw's frank-large
has swapped runtimes seven times in search of a better one. This one
ships no prebuilt Linux binary, so the runtime is BUILT FROM SOURCE on
the host and the payload, port, alias and client contract stay exactly
where they are.
Runtime: gufo v0.1.1 (b0f8673) at /home/dank/GitPackages/gufo-v0.1.1,
built by the new scripts/build-gufo.sh. It links the host ROCm 7.2.4
through its RPATH — the opposite of the unsloth prebuilt it replaces,
which carried its own — so two host workarounds are encoded in that
script: Ubuntu's librocwmma-dev installs a truncated rocWMMA header set
that shadows AMD's and cannot be overridden with -I in HIP mode, and the
ROCm clang prefers Ubuntu 26.04's GCC 16 headers over the GCC 15 whose
libstdc++ is actually installed.
Shape is unchanged: one session, full 262144 ctx, the same UD-Q8_K_XL
target and the same DFlash2 drafter, greedy, text-only. Speculation
moves from llama.cpp's fixed depth 4 to gufo's adaptive block policy
capped at 7 proposals.
Client-contract deltas, all documented: the chat template is compiled
into the binary (no --chat-template-file, so the Qwen-Sharp 73k-token
failure mode is gone), reasoning_effort now takes the full OpenAI set
(so models.yml's reasoningEffortMap fold is no longer required — kept
anyway, so this commit does not also move every high role from xhigh to
high effort), and there is no KV-quant or prompt-cache-RAM flag.
The test seam now runs the serve script against a stub engine and
asserts on the argv it actually execs, instead of grepping the script's
text. The deploy handler gates on the runtime binary and returns before
it stops the serving unit.
Measured trade, from gufo's own Q8_K_XL tables against llama.cpp
b11069: prefill +37% to +80%, cold load 4.5x faster, decode +7% at zero
depth — but -14% at 65,536 tokens of depth and -24% at 131,072. On the
8-bit target the crossover is ~32k, and frank-large runs one 262,144
conversation. The Q4_K_XL target shows no such regression.
Answers Frank's question: given the 2x R9700 box, which of DeepSeek-V4.1-Flash,
MiMo-V2.6-Flash-MOPD and GLM-5.3-Flash runs best, on what runtime, and can
Affinity / vllm-radiance / FreeToken-AMD be modified to run them fast.
Sizing, measured from the HF API rather than the model cards:
- DS4.1 payload 475.27 GiB, of which 189.13 GiB is Engram (two 94.56 GiB
shards, the layer 1 and 14 lookup tables). Lucebox never maps them, so
they do not evict the expert page cache.
- 240 routed experts x ~11 MB per token at the 37-of-384-per-layer residency
the hot stack can hold = ~2.4 GB across PCIe per token. At 55 GB/s real on
Gen5 x8 that is a ~23 tok/s ceiling, under the 35 tok/s goal before any
attention or dense work.
- The backend refuses layer-major prefill for V4.1, so the only legal
prefill is token-by-token; 1000 t/s is ~20x away.
Affinity (Codeberg StillDeadcode) builds clean here (hipcc 23, gfx1201, zero
errors) and claims 110 tok/s for the 284B V4-Flash, because the 101 GiB
container leaves the cold expert set in a pinned host pool in RAM. V4.1 is
2.5x too big to repeat that. Port scope, from both checkpoints' real
safetensors indices: expert machinery ports unchanged (identical tensor
naming, 15,360 tensors at 40x384), 26/32 per-layer tensors and 34/35 config
keys present. Five gaps - Engram, the disjoint {0,1,2} vs {0,4,128} ratio
vocabulary, CED, the DSpark draft geometry, vision. num_hash_layers is the
only key V4.1 lacks, so model.h:36's default of 3 silently survives and
would route layers 0-2 through a table that does not exist.
Also corrects: Dankserver RAM 60 -> 246 GiB (stale since the 2026-09-20 board
swap) and storage; the top-level index credited V4.1 with V4's geometry
(43 layers / 256 experts vs the actual 40 / 384).
New ACTIVE runbook (setup/doctor PASS=34/FAIL=0, UVA transport verified
in serving workers, wire-verified alias/reasoning/metrics), index +
vault entries flipped: llama.cpp b11030 endpoint RETIRED 2026-09-27 to
rollback target. Topology diagram updated.
download-frank-primary-fp8.sh + the fp8 manifest fetched the vLLM 0.30
payload that never deployed; the R9V cutover's payload is the R9V setup
flow's (packages + PLE, state-gated). Scripts move to old/ per policy.
Replace the vLLM 0.30 UVA-offload wiring (never deployed; llama.cpp
b11030 endpoint replaced by Frank's order, downtime window open) with
the R9V radiance-vLLM shape on the same root unit:
- serve-frank-primary.sh: layer the on-host qualified setup state
(104 tuned R9V_* keys, /home/dank/r9v-state/uncensored/setup.json)
under repo production overrides, then exec the fork's own launcher
through the docker->podman shim and block on docker wait so a crashed
engine is a nonzero ExecStart (launch.sh --detach alone would fake
green). Gates: pinned image by OCI manifest digest in RepoDigests,
PLE at pinned byte count (stat -c %s), all GGUF shards + mmproj.
- frank-primary.env: thin production overrides only (LAN bind 0.0.0.0,
:7770, alias frank-primary, container frank-primary-r9v, GPU BDFs);
tuned engine shape stays in the setup state by design.
- frank-primary.service: same root-unit shape (User=dank,
LimitMEMLOCK=infinity for the tiered-expert pin), plus ExecStop
podman stop — docker wait cannot stop the container child.
- deploy-remote.sh deploy_llm_primary: state-driven gates (state ready,
image digest present in rootless storage, PLE bytes, shard files)
replace the fp030 shard-index gate; false-green MainPID check kept.
- tests/frank_primary_unit.bats: seam rewritten for the R9V shape.
Unit restart SIGKILLs conmon before --rm cleanup runs, leaving the container
name registered: every restart then dies on name-in-use while the orphan
holds the port and GPUs (observed live 2026-09-27, NRestarts=6 with the
container Up and the unit auto-restarting). The serve script now clears its
own name before exec podman run; the deployer stops the unit and removes
the container BEFORE waiting on the port.
HOME inside the container pointed at the root-owned WORKDIR /app, so
vLLM's registry cache write hit PermissionError; HOME now points at the
cache volume.
The vLLM cutover itself is blocked on a structural wall (AMD Qwen4Exp impl
creates the 51.2B PLE table as a bf16 on-GPU param: 47.69 GiB transient on a
31.86 GiB card; the pinned-host UVA path that fits this hardware is
CUDA-gated). llama.cpp is restored live at :7770; host HEAD unchanged.
Frank