feat(ana-ml2): replace Qwen3.5-9B vision with Qwen3.6-35B-A3B FP8 on GPU 1

Retire qwen35-vl (Qwen3.5-9B); add qwen36-vl serving the official FP8 Qwen3.6-35B-A3B vision MoE on :8007 under its TRUE name only — no alias. qwen3.5-9b-fp8 is killed at vLLM AND the litellm gateway (404/400); a model is never served under a prior model's name. Consumer (comfy-dev/arbo) notified + migrated; arbo vkeys flipped to all-proxy-models; shared all-agents-local key repointed to qwen3.6-35b-a3b. GPU-1 rebalance for the heavier FP8 weights (~34 GB): granite 0.35->0.24 / 131K->64K, embed/rerank 0.05->0.03 (reclaimed util-reservation waste). Verified: vision correct, 20-concurrent/endpoint load test = no OOM (~7.5 GB headroom). Drop the llama-swap qwen3.5-9b GPU-0 pin (GPU 0 freed for the creative-writing hot-swap card). NVFP4 was the lighter fit (~21 GB) but its vLLM ModelOpt-MoE loader is broken (KeyError w2_input_scale / lm_head.input_scale, vllm #44081); revisit when fixed.
2026-06-14 14:41:49 -07:00
parent b45d0cd86d
commit a0fed13801
8 changed files with 168 additions and 221 deletions
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+# qwen36-vl — Qwen3.6-35B-A3B vision-language MoE (official FP8) on ana-ml2.
+#
+# Replaces the qwen35-vl stack (Qwen3.5-9B) 2026-06-14. Co-located on GPU 1 with
+# the granite summarizer + embed/rerank/reward trio (GPU 0 stays free for the
+# llama-swap creative-writing hot-swap card). Serves on :8007.
+#
+# WHY official FP8 (not NVFP4): NVFP4 (nvidia/Qwen3.6-35B-A3B-NVFP4, ~21 GB) is the
+# lighter fit but its vLLM ModelOpt-MoE loader is BROKEN as of 0.19.1/0.22.0
+# (KeyError w2_input_scale / lm_head.input_scale — vLLM #44081). The official
+# Qwen pre-quantized FP8 (~34 GB weights) loads clean on :latest and — unlike
+# the old qwen35-vl — needs NO pinned nightly digest: that hack existed because
+# vLLM DYNAMIC `--quantization fp8` quantized the vision tower to noise. This
+# checkpoint is PRE-quantized, so we OMIT --quantization (vLLM auto-detects the
+# checkpoint's own fp8) and the vision tower is preserved. Validated 2026-06-14
+# on GPU 0: loads in 34.2 GiB, image test returns correct ("Blue"). Revisit
+# NVFP4 (frees ~13 GB) once vLLM's loader is fixed.
+#
+# NAMING: served ONLY as its TRUE name `qwen3.6-35b-a3b`. A model is never aliased
+# under a prior model's name — a caller asking for `qwen3.5-9b-fp8` (a 9B dense)
+# must NOT be silently handed this 35B-A3B MoE; that's a downstream-confusion
+# footgun. The legacy `qwen3.5-9b-fp8` name is RETIRED. Consumers (Arbo's vision
+# hero-judge, stacks/arbo v0.11.3+) migrate to `qwen3.6-35b-a3b` — they 404 on the
+# old name until they repoint, which is the correct loud signal (notified 2026-06-14).
+#
+# WHY util 0.42 / max-len 131072: hybrid attn (10 of 40 layers full-attn, ~10 KB/
+# tok KV) → KV is cheap, so big context is nearly free; the 34 GB weights are the
+# cost. 0.42 (~40 GB) = weights + graph + generous KV. Granite drops to 0.25/64K
+# to make room (the FP8-vs-maxed-granite tradeoff, operator-approved 2026-06-14).
+#
+# All tunables live in .env — edit that, not this file.
+
+name: qwen36-vl
+
+services:
+  vllm-qwen36:
+    image: ${QWEN_IMAGE}
+    container_name: ${QWEN_CONTAINER_NAME}
+    restart: unless-stopped
+    ipc: host
+    ports:
+      - "${QWEN_PORT}:8000"
+    volumes:
+      - /tank/aimodels/huggingface:/hfcache
+    environment:
+      - HF_HOME=/hfcache
+      - HF_HUB_CACHE=/hfcache/hub
+      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
+      - VLLM_API_KEY=${API_KEY:-}
+    command:
+      - ${QWEN_MODEL}
+      # Pre-quantized FP8 checkpoint → NO --quantization (vLLM auto-detects; a
+      # forced flag would re-quantize the vision tower to noise, see header).
+      - --served-model-name
+      - qwen3.6-35b-a3b
+      - --host
+      - 0.0.0.0
+      - --port
+      - "8000"
+      - --gpu-memory-utilization
+      - ${QWEN_GPU_MEM_UTIL}
+      - --max-model-len
+      - ${QWEN_MAX_MODEL_LEN}
+      # Cap concurrency: vLLM warms the sampler with max_num_seqs dummy requests,
+      # and this model's 248K vocab makes that warmup tensor huge — the default
+      # 1024 OOMs on a shared GPU even though weights+KV fit. 32 is ample for a
+      # vision endpoint (the summarizer carries the concurrency, not this).
+      - --max-num-seqs
+      - ${QWEN_MAX_NUM_SEQS}
+      - --kv-cache-dtype
+      - fp8
+      - --trust-remote-code
+      - --dtype
+      - auto
+      - --enable-prefix-caching
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids:
+                - "${QWEN_GPU_ID}"
+              capabilities:
+                - gpu
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 300s
+    networks:
+      - tnet
+    labels:
+      - homepage.group=AI Systems
+      - homepage.name=Qwen3.6-35B-A3B VL (FP8)
+      - homepage.icon=mdi-image-search
+      - homepage.description=Qwen3.6-35B-A3B vision-language MoE (FP8) via vLLM (ana-ml2)
+      - homepage.href=http://10.250.50.54:${QWEN_PORT}/docs
+
+networks:
+  tnet:
+    name: traefik-net
+    external: true