# mog-sec — the pen-test seat on ana-ml2 GPU 0 (:8019). # # MOVED GPU 1 -> GPU 0 on 2026-08-28 (operator-directed). GPU 1 carries the five # resident fleet seats (gen 46 GB + embed + coder + rerank + reward = ~69.9 GB of # 97.9), leaving ~28 GB — less than the ~51 GB this seat reserves at # MOG_GPU_MEM_UTIL=0.52, so it could no longer start there. GPU 0 has been idle # since run 3c was stopped. # # ⚠ POWER: bringing this up re-arms the two-GPU load condition that tripped the # Anaheim rack breaker on 2026-08-26. One circuit feeds the whole rack including # ana-gw and ana-wg, so a trip costs the site AND the remote path in. Idle draw is # negligible (~6-13 W/card); the risk is sec and gen under concurrent load. # # Serves Blackfrost-Research/M.O.G.-SEC-27B-1M-CTX-BF16 (stock-Qwen3.8-27B-based, # vision-intact Qwen3_5ForConditionalGeneration, base-graft MTP head), quantized # in-house to mixed NVFP4 W4A4 (bulk MLP) + FP8 W8A8 (attn / linear_attn / lm_head / # top MLP layers), with the bf16 MTP head grafted back. ⚠ the grafted MTP requires # `re:^mtp.*` in config.json quantization_config.ignore or vLLM loads it uninitialised # (0% accept) — handled by post_quant.py, verified present at build time. # # CONTEXT: served at native 262K, NOT the card's 1M. The 1M needs YaRN rope_scaling # (absent from the weights' config) plus the SGLang/DFlash2 attention path the repo # ships a deployment kit for — neither is our vLLM serving surface. 262K is the honest # native ceiling here; a real 1M seat would be a separate SGLang project. # # Two served-names (base + `-thinking`): LiteLLM keys deployments by (model, api_base), # so mog-sec and mog-sec-reasoning use distinct names to avoid the shared-config # enable_thinking clobber. Same pinned nightly as the gen seat (carries the #51113 # qwen3_5_mtp x GDN partial-accept fix that the MTP-on config depends on). name: mog-sec services: vllm-mog-sec: image: ${MOG_IMAGE:-vllm/vllm-openai:latest} container_name: ${MOG_CONTAINER_NAME:-vllm-mog-sec} restart: unless-stopped ipc: host ports: - "${MOG_PORT:-8019}:8000" volumes: - /tank/aimodels/huggingface:/hfcache - ${MOG_MODEL:-/tank/aimodels/mog-sec-27b-nvfp4-mixed}:/model:ro # DFlash2 speculative drafter. Mounted unconditionally — it is inert if # MOG_SPEC_CONFIG selects an MTP method that does not reference /drafter. - ${MOG_DRAFT_MODEL:-/tank/aimodels/qwen38-27b-dflash2-drafter}:/drafter:ro environment: - HF_HOME=/hfcache - HF_HUB_CACHE=/hfcache/hub - VLLM_API_KEY=${API_KEY:-} # ⚠ DEFAULTS TO UNSET, deliberately. This seat previously hardcoded # PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True. The DFlash2 config # validated 2026-08-22 ran WITHOUT it, and the quant playbook §3.10 # records expandable_segments corrupting retained tensors in another # context. Do not re-enable it casually — that would ship a variable the # tested configuration did not have. - PYTORCH_CUDA_ALLOC_CONF=${MOG_ALLOC_CONF:-} command: - /model - --served-model-name - ${MOG_SERVED_NAME:-mog-sec-27b} - ${MOG_SERVED_NAME_THINK:-mog-sec-27b-thinking} - --host - 0.0.0.0 - --port - "8000" - --quantization - ${MOG_QUANT:-compressed-tensors} - --gpu-memory-utilization - ${MOG_GPU_MEM_UTIL:-0.44} - --max-model-len - ${MOG_MAX_MODEL_LEN:-262144} - --max-num-seqs - ${MOG_MAX_NUM_SEQS:-16} - --max-num-batched-tokens # ⚠ Raising this costs peak-activation VRAM straight out of the KV pool # (measured 2026-08-22: 16384 -> 32768 cost ~3 GiB of KV for no benefit). - ${MOG_MAX_NUM_BATCHED_TOKENS:-16384} - --trust-remote-code - --dtype - auto - --mamba-cache-dtype - float32 - --kv-cache-dtype - ${MOG_KV_CACHE_DTYPE:-fp8} - --enable-prefix-caching - --enable-chunked-prefill - --limit-mm-per-prompt - '${MOG_LIMIT_MM:-{"image": 4}}' # ⚠ MANDATORY on a newer vLLM. The model's own preprocessor_config.json # declares size.longest_edge = 16777216 px (4096x4096), which expands to # 16384 image tokens — one image eating 6% of a 262K context, and enough # to kill startup on builds that enforce the text-vs-ids count check. # This caps the dummy profiling image AND real images. Cost scales as # (edge/patch)^2 / merge^2, so 2048x2048 -> ~5125 tokens. - --mm-processor-kwargs - '${MOG_MM_PROCESSOR_KWARGS:-{"size": {"longest_edge": 4194304, "shortest_edge": 65536}}}' - --reasoning-parser - ${MOG_REASONING_PARSER:-qwen3} - --default-chat-template-kwargs - '{"reasoning_effort": "${MOG_REASONING_EFFORT:-medium}"}' - --enable-auto-tool-choice - --tool-call-parser - qwen3_coder # ONE env var carrying the whole JSON, because the two speculative shapes # are not interchangeable: dflash needs a "model" pointing at the drafter, # MTP must NOT have one. A method+tokens template cannot express both. # DFlash2 : {"method": "dflash", "model": "/drafter", "num_speculative_tokens": 7} # MTP : {"method": "qwen3_5_mtp", "num_speculative_tokens": 3} # ⚠ Comparing the two requires matching num_speculative_tokens — see the # quant playbook §5.1: MTP runs a single-module head autoregressively, so # deeper k improves acceptance and DESTROYS throughput. - --speculative-config - '${MOG_SPEC_CONFIG:-{"method": "qwen3_5_mtp", "num_speculative_tokens": 3}}' deploy: resources: reservations: devices: - driver: nvidia device_ids: - "${MOG_GPU_ID:-0}" capabilities: - gpu healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8000/health"] interval: 30s timeout: 10s retries: 3 start_period: 900s networks: - tnet labels: - homepage.group=AI - Inference - homepage.name=M.O.G.-SEC 27B (pen-test) - homepage.icon=mdi-shield-lock - homepage.description=Uncensored security model, Qwen3.8-27B NVFP4+MTP, 262K — the `mog-sec` seat (ana-ml2 GPU 0) - homepage.href=http://10.250.50.54:${MOG_PORT:-8019}/docs networks: tnet: name: traefik-net external: true