The 0.55 configuration booted cleanly, passed every startup check, and then
killed the engine on an ordinary request of roughly 20k tokens:
torch.OutOfMemoryError allocating 614 MiB in the GDN causal_conv1d path with
443 MiB free. The container self-recovered via restart: unless-stopped.
Context length was not the cause. gpu-memory-utilization is not a hard
reservation. Weights and KV are reserved up front, but activation memory is
allocated on demand and the profile that sizes it uses dummy data at
max_num_batched_tokens. The profile predicted 3.62 GiB; the process sat about
1 GiB over its entire budget even at idle. With six vLLM containers sharing
GPU1 there was no slack left to grow into, and a modest allocation lost the
race. 0.52 leaves roughly 7.7 GiB free against 4.6 GiB at 0.55.
Records a measurement that governs how max-model-len can be chosen at all:
the KV pool varies between boots. Two consecutive boots at 0.52 produced
425,663 and 453,600 tokens, a 6.6 percent swing, because the pool is sized
from free memory at startup and that depends on what the other tenants hold
at that instant. 420,000 sits under the lower observation so the seat starts
in both conditions. Sizing to the higher one would boot today and refuse
tomorrow, which is why the value was measured across boots rather than
estimated from one.
Concurrency of about 1.0 to 1.08x means one request at full length. Shorter
requests still batch normally, since vLLM schedules by tokens rather than by
max-model-len.
Operator approved after real-use testing. The experimental standalone
container is retired and stacks/mog-sec is canonical again, with
restart: unless-stopped so the configuration survives a reboot.
Cutover verified against the container it replaces: KV pool 526,617 tokens
at 1.10x concurrency, identical; zero restarts; both gateway aliases
serving; DFlash2 confirmed drafting at k=7 with 231 draft tokens over 33
drafts; vision working at 2048x2048.
One variable was deliberately dropped rather than carried over. The previous
stack hardcoded PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True, the
validated container never set it, and the quant playbook records
expandable_segments corrupting retained tensors in another context. The
compose now defaults it empty via MOG_ALLOC_CONF. Promoting the stack as it
stood would have shipped a variable the tested configuration did not have.
The speculative config moves into a single MOG_SPEC_CONFIG carrying the
whole JSON, because the two shapes are not interchangeable: dflash requires
a model pointing at the drafter and MTP must not have one, so a
method-plus-tokens template cannot express both. Also parameterised:
MOG_DRAFT_MODEL, MOG_MM_PROCESSOR_KWARGS, MOG_MAX_NUM_BATCHED_TOKENS.
The mm-processor image cap is now mandatory rather than incidental. The
model's own preprocessor declares 4096x4096, which expands to 16384 image
tokens and kills startup on builds that enforce the image-token count check.
Adds the .env.example this stack never had, carrying the measured rationale
for each value and the one-line rollback.
Autonomous overnight run under the operator's full-autonomy grant. End state:
fleet up, gen seat untouched, a new verified pen-test seat serving where fable was.
PPL on the orcarouter gen seat (fable downed to free GPU1 for a nospec probe,
probe torn down after): mean 7.07 / median 5.76, within noise of heresy 6.910 /
5.625 and identical to our recipe's usual 7.059. The gen-seat search is settled.
M.O.G.-SEC: chose Blackfrost-Research/M.O.G.-SEC-27B-1M-CTX-BF16 (rev deede677)
over the pre-made ModelOpt NVFP4, which was disqualified on W4A4 4-bit activations
(the AEON degradation mode, catastrophic on a 1M-context model), zero MTP tensors,
and ModelOpt format. Pulled, format-screened (P(<think>) 1.11e-05, clean), quanted
in-house to mixed NVFP4+FP8 (23.4 GB, MTP + vision preserved), and served in the
retired fable slot.
stacks/mog-sec ana-ml2 GPU1 :8019, KV 418,218 tok / 1.60x @ 262K
aliases mog-sec (non-thinking), mog-sec-reasoning (thinking)
gates surface 6/6, MTP 55.3%, format 0/15 leak, vision 7/3/1,
capability 4/4 (delivers offensive-security content)
Served at native 262K, NOT the card's 1M -- the 1M needs YaRN (absent from the
weights' config) plus the SGLang/DFlash2 path the repo ships a deployment kit for,
neither of which is our vLLM surface. A real 1M seat is a separate SGLang project.
Retired char-rp-reasoning + char-rp-fable (zero traffic, pointed at the downed
fable :8019; now 404 cleanly, not repointed -- a security model is not an RP model).
char-rp (meromero) untouched. Vision preprocessor built from the model's own
image_processor block, same trick as the MeroMero seat.
GPU0 seats (gen, meromero) were untouched and healthy throughout. The quant ran in
GPU1 free space with no production seat stopped except fable, which was replaced.
vh