LFM2.5 is </think>-delimited (opening tag in prompt); deepseek_r1 splits
reasoning into reasoning_content so content is the clean post-</think>
answer. Re-smoke: content valid JSON + reasoning_content populated. License
production-cleared (operator <$10M ruling), still out of routing per the
measurement gate.
vllm-lfm25 on ana-ml2 GPU1 :8021 (LiquidAI/LFM2.5-2.6B, util 0.09 into
unreserved slack, max-len 16384, no reasoning-parser so content is non-empty).
LiteLLM alias lfm2.5-2.6b with vendor sampling baked as default (temp 0.1;
top_k 50 + repetition_penalty 1.1 via extra_body). Eval-only, not in any
routing chain, pending operator ruling on LFM Open License production use.
granite (fleet fan-out summarizer/classifier) had no explicit --max-num-seqs,
so vLLM V1 resolved it to 128 — which caps concurrency BELOW granite's own KV
bound (~192 concurrent @ 1K-token calls, more for shorter classify calls).
Pinned it very high (1024) so the KV pool is the only bound; VRAM-neutral
(the KV pool is util-bound, unchanged). Added the flag to the granite command
+ GRANITE_MAX_NUM_SEQS to the env template. Live applied + verified
(resolved max_num_seqs=1024, seat healthy).
Move the ~22-service flat "AI Systems" group off the Main tab into a new
four-tab layout (Main / AI / Infrastructure / Toolchain). The AI tab sorts
the inference fleet by function into seven groups:
AI - Inference gen, char-rp, char-rp-reasoning, Granite summarizer
AI - Eval & Retrieval Selene, Skywork Reward, Qwen3 rerank/embed, image-bench
AI - Gateways & Chat LiteLLM, Asset Engine, Gateway Chat, Open WebUI, ...
AI - Speech (TTS) Chatterbox Fast, Kokoro, mOrpheus
AI - Audio Tools Parakeet ASR, YT Voice Clipper
AI - Image & Media ComfyUI, Arbo
AI - Dormant stopped rollback seats + retired auditions
Relabel each stack's homepage.group so canonical stacks/ matches the live
containers on ana-ml2, ana-docker, and irv-ml1. Dormant stacks were refreshed
with `docker compose up --no-start` so they carry the new label while staying
stopped (compose-start rollback preserved). settings.yaml drives tab/order/
columns; services.yaml and README updated to the new scheme.
ana-ml2 was upgraded 2026-06 from dual RTX 6000 Ada (46GB, cc 8.9) to
dual RTX PRO 6000 Blackwell Max-Q (96GB, cc 12.0 / sm_120). Update the
stale hardware facts across the workspace:
- CLAUDE.md servers table row
- servers/ana-ml2/README.md hardware spec (+ refreshed system-details.txt)
- stacks/vllm compose + .env.example FP8/KV comments (Ada cc 8.9 -> Blackwell cc 12.0)
- stacks/llama-swap config VRAM-budget comment (48GB -> 96GB, GPU-0 pin)
Also corrects the adjacent stale 'Phi-4-mini' comment in the granite
service block (the service has been Granite 4.1 8B since 34a43a0).
Doc/comment-only; no runtime change.
Benched granite prefix caching at ~6.5x faster TTFT (45ms cached vs 292ms
uncached) on a shared ~4.5k-token summarizer template. granite already had it
on by vLLM-v1 default; pinned explicit so a version flip can't silently disable
it. qwen (nightly) defaulted it OFF -> flipped on (free for the text-chat path,
marginal for vision where each image is a distinct prefix). Soft/evictable KV,
zero memory change (GPU1 still ~3.7GB free), all 5 services healthy.
Granite 4.1 8B beat phi4-mini on precision in brokkr's R15 P03 eval, so it's
the new production summarizer/dreamer for nevermore.
- vllm-phi4 -> vllm-granite: official IBM FP8 (ibm-granite/granite-4.1-8b-fp8,
compressed-tensors), GPU 1, 50K ctx, FP8-KV, CUDA graphs. Same :8004 slot.
- GPU 1 retune: the embed/rerank/reward trio was over-provisioned (embed ran a
5.89x KV pool, reward 3.90x). Trimmed utils 0.20/0.20/0.30 -> 0.07/0.07/0.18,
freeing ~10 GB so granite runs with CUDA graphs (not --enforce-eager) and
keeps ~10 GB free as a hedge for future Granite text-LoRAs (--enable-lora).
- LiteLLM: phi4-mini model_list entry -> granite-4.1-8b (hosted_vllm @ :8004);
explicit entry shadows the '*' wildcard's llama-swap route.
- nevermore repointed (LLAMA_SWAP_MODEL=granite-4.1-8b via the gateway) live.
Verified end-to-end: vLLM :8004 generates, gateway routes (gateway-granite-ok),
KV 86,768 tokens/1.69x at 50K, 0 restarts, GPU 1 10.3 GB free.
Operator chose option (ii): keep the OFFICIAL Phi-4 format globally rather than
impose Ollama's leaner scaffold on every phi4 consumer. Removes the
--chat-template override + the conf/phi4-chat-template.jinja file (90e08f0).
vLLM now uses the tokenizer's built-in template (system <|end|> present);
verified 7-token render via tokenize/detokenize. brokkr re-baselines its R15
canonical on the official scaffold so baseline == production.
vLLM's official Phi-4 tokenizer template emits <|end|> after the system turn;
Ollama's does not. That single boundary token regressed brokkr's R15 P02
admission eval (type macro-F1 -33pp) vs the Ollama-measured canonical, while
valid_format held at 1.0. Operator chose to make vLLM match Ollama's leaner
scaffold globally (baseline == production). Adds conf/phi4-chat-template.jinja
(drops the system <|end|>) + mounts it + --chat-template on vllm-phi4. Applied
prompt verified via tokenize/detokenize; brokkr re-smokes probe_vllm.yaml.
Two related changes shipped together. The stack rename is independent
but adding `vllm-reward` to the existing `vllm-qwen3` would have made
that name actively misleading.
**Rename:** `stacks/vllm-qwen3/ → stacks/vllm/`. Updated all in-repo
references (README.md root, servers/ana-ml2/, stacks/llama-swap/,
configs/restic/ana-ml2/, docs/runbooks/disaster-recovery.md). Two
intentional history mentions retained (servers/ana-ml2 + stacks/vllm
README).
**Add `vllm-reward` service:** serves Skywork-Reward-V2-Llama-3.1-8B-AWQ
on port 8003. The AWQ output is a locally-quantized model (not from HF),
so bind-mounts `/tank/aimodels/llm:/local-models:ro` rather than the
shared HF cache. Model config.json declares LlamaForSequenceClassification
which vLLM's pooling runner picks up automatically — produces a single
reward score per input via /classify.
**Flag note:** the user's spec listed `--task classify`, but vLLM 0.19.1
deprecated --task in favor of --runner pooling (model architecture in
config.json drives the classification head). Compose uses --runner
pooling with a comment explaining the substitution.
**GPU memory:** no rebalance needed — production had already tuned
EMBED/RERANK down from 0.40 to 0.20 each (canonical .env.example now
matches reality). Adding REWARD at 0.30 totals 0.70, leaving ~14 GB
headroom on the 48 GB Ada.
**Server-side:** brought existing vllm-qwen3 down, mv'd
/opt/docker/compose/vllm-qwen3 → /opt/docker/compose/vllm, appended
REWARD_* lines to existing .env (preserving API_KEY/HF_TOKEN), deployed
new compose via scripts/deploy-stack.sh, brought all 3 services up.
**Smoke tests:**
- /health on 8001/8002/8003 → 200
- /v1/models on 8003 → lists Skywork/Skywork-Reward-V2-Llama-3.1-8B-AWQ
with max_model_len 16384
- /classify with a sample conversation → returns LABEL_0 with prob 0.9999
(single-output regression-style reward score, expected shape for a
reward model)
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