diff --git a/stacks/litellm/conf/config.yaml b/stacks/litellm/conf/config.yaml
index 22d6c1a..37c0ee1 100644
--- a/stacks/litellm/conf/config.yaml
+++ b/stacks/litellm/conf/config.yaml
@@ -30,6 +30,17 @@ model_list:
     model_info:
       mode: chat
 
+  # --- Qwen3.5-9B vision-language (FP8) — vision + chat. vLLM on ana-ml2 GPU 1,
+  #     nightly-pinned (vision-FP8 exclusion fix), :8007. Explicit entry shadows
+  #     the "*" wildcard llama-swap route. ---
+  - model_name: qwen3.5-9b-fp8
+    litellm_params:
+      model: hosted_vllm/qwen3.5-9b-fp8
+      api_base: http://10.250.50.54:8007/v1
+      api_key: os.environ/VLLM_API_KEY
+    model_info:
+      mode: chat
+
   # --- Qwen3 embeddings ---
   - model_name: qwen3-embedding
     litellm_params:
diff --git a/stacks/qwen35-vl/.env.example b/stacks/qwen35-vl/.env.example
new file mode 100644
index 0000000..5b145d0
--- /dev/null
+++ b/stacks/qwen35-vl/.env.example
@@ -0,0 +1,25 @@
+# Qwen3.5-9B VL (FP8) on ana-ml2 — copy to .env on the host and fill.
+# Real .env lives on ana-ml2 at /opt/docker/compose/qwen35-vl/.env (gitignored).
+
+# Pinned nightly digest — carries the Qwen3.5-VL vision-FP8 exclusion fix that
+# :latest (v0.19.1) lacks. Re-pin to :latest once the fix reaches a stable
+# release (see README + compose header).
+QWEN_IMAGE=vllm/vllm-openai@sha256:49211ab2155b21a2dc35f3583f5b545f5e55e77daf8f86df49977c71d5f2f528
+
+QWEN_CONTAINER_NAME=vllm-qwen35
+QWEN_MODEL=Qwen/Qwen3.5-9B
+QWEN_SERVED_NAME=qwen3.5-9b-fp8
+QWEN_PORT=8007
+
+# GPU 1 = shared with the granite summarizer + embed/rerank/reward trio.
+# GPU 0 is kept free for hot-reloading large models.
+QWEN_GPU_ID=1
+
+# 0.40 (~38 GB) — above the ~34 GB start floor, ~10 GB card headroom over prod.
+# On this shared card vLLM needs free >= util*total, so util is capped ~0.51.
+QWEN_GPU_MEM_UTIL=0.40
+QWEN_MAX_MODEL_LEN=32768
+
+# Optional
+HF_TOKEN=
+API_KEY=
diff --git a/stacks/qwen35-vl/README.md b/stacks/qwen35-vl/README.md
new file mode 100644
index 0000000..dfc9dcf
--- /dev/null
+++ b/stacks/qwen35-vl/README.md
@@ -0,0 +1,57 @@
+# qwen35-vl — Qwen3.5-9B vision-language (FP8) on ana-ml2
+
+Qwen3.5-9B, a hybrid GDN + vision-language model, served **FP8** on ana-ml2
+**GPU 1**, fronted by the LiteLLM gateway as `qwen3.5-9b-fp8`. Image + video
+understanding and chat. The language model is FP8; the **vision tower stays
+BF16** (see below).
+
+## Placement
+- **GPU 1**, co-located with the granite summarizer + embed/rerank/reward trio.
+  **GPU 0 is deliberately kept free** for hot-reloading large models.
+- Port **8007**. Gateway: `qwen3.5-9b-fp8` via LiteLLM (`ana-docker:4000`).
+- Container `vllm-qwen35`, compose project `qwen35-vl`.
+
+## Why a pinned nightly digest (not :latest)
+vLLM `:latest` (v0.19.1) quantizes the Qwen3.5-VL **vision tower** under
+`--quantization fp8` → garbage vision (the LM answers text fine but "sees"
+noise — verified: it described the two-cats COCO image as "a 6×6 grid of gray
+squares"). The **nightly** correctly excludes the vision tower from FP8, so
+vision works while the LM still gets the FP8 throughput/VRAM win (BF16 vision
+read perfectly: "two cats on a bright pink surface… two remote controls").
+
+We pin the exact nightly digest (`sha256:49211ab2…`) for reproducibility — a
+moving `:nightly` tag would silently change the engine. **WATCH:** when the
+vision-FP8 exclusion lands in a stable release, re-pin to `:latest` and delete
+this note.
+
+## Why util 0.40
+The model needs ~34 GB just to **start** at 32k context (FP8 weights + BF16
+vision tower + CUDA-graph capture + 32k memory profiling). On shared GPU 1
+(prod uses ~46 GB, ~48 GB free) this vLLM build requires `free >= util*total`,
+capping util at ~0.51 here; **0.40 (~38 GB)** sits above the ~34 GB floor with
+~10 GB card headroom and reports ~20× max concurrency at 32k. (Empty-GPU floor
+was 0.35; below ~0.33 it crashes with "no KV blocks".) To shrink the footprint,
+lower `QWEN_MAX_MODEL_LEN` (vision queries rarely need 32k) rather than util.
+
+## Deploy
+```
+scripts/deploy-stack.sh ana-ml2 qwen35-vl     # or scp compose to the host
+# on ana-ml2: create /opt/docker/compose/qwen35-vl/.env from .env.example, then:
+cd /opt/docker/compose/qwen35-vl && docker compose up -d
+```
+
+## Smoke test (incl. vision)
+```bash
+curl -s http://10.250.50.54:8007/v1/chat/completions -H 'Content-Type: application/json' \
+  -d '{"model":"qwen3.5-9b-fp8","messages":[{"role":"user","content":[
+    {"type":"text","text":"How many cats and what surface are they on?"},
+    {"type":"image_url","image_url":{"url":"http://images.cocodataset.org/val2017/000000039769.jpg"}}]}],
+    "max_tokens":120,"chat_template_kwargs":{"enable_thinking":false}}'
+```
+It's a **thinking** model (emits a reasoning trace by default) — pass
+`chat_template_kwargs:{"enable_thinking":false}` for terse answers.
+
+## Related
+- The NVFP4 path for this model was abandoned — FP8 is the answer on Blackwell
+  (W4A4 collapses, weight-only 4-bit doesn't accelerate). The AxionML NVFP4
+  community quant + joninco SGLang fork were the NVFP4 attempt; not used.
diff --git a/stacks/qwen35-vl/compose.yaml b/stacks/qwen35-vl/compose.yaml
new file mode 100644
index 0000000..5377998
--- /dev/null
+++ b/stacks/qwen35-vl/compose.yaml
@@ -0,0 +1,84 @@
+# qwen35-vl — Qwen3.5-9B vision-language model (FP8) on ana-ml2.
+#
+# Co-located on GPU 1 with the granite summarizer + embed/rerank/reward trio
+# (GPU 0 is deliberately kept free for hot-reloading large models). Serves on
+# :8007, fronted by the LiteLLM gateway as `qwen3.5-9b-fp8`.
+#
+# WHY A PINNED NIGHTLY DIGEST (not :latest): vLLM :latest (v0.19.1) quantizes
+# the Qwen3.5-VL *vision tower* under --quantization fp8, producing garbage
+# vision output (the language model is unaffected — it answers text fine but
+# "sees" noise). The nightly correctly excludes the vision tower, so vision
+# works while the LM still gets the FP8 throughput/VRAM win. We pin the exact
+# nightly digest for reproducibility — a moving :nightly tag would silently
+# change the engine. WATCH: once the vision-FP8 exclusion lands in a stable
+# release, re-pin to :latest and drop this note.
+#
+# WHY util 0.40 (not the trio's tiny values): the model needs ~34 GB just to
+# start at 32k context (FP8 weights + BF16 vision tower + graph capture + 32k
+# profiling). On shared GPU 1 (prod uses ~46 GB, ~48 GB free) this vLLM build
+# requires free >= util*total, capping util at ~0.51 here; 0.40 (~38 GB) sits
+# above the ~34 GB floor with ~10 GB card headroom.
+#
+# All tunables live in .env — edit that, not this file.
+
+name: qwen35-vl
+
+services:
+  vllm-qwen35:
+    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}
+      - --served-model-name
+      - ${QWEN_SERVED_NAME}
+      - --quantization
+      - fp8
+      - --host
+      - 0.0.0.0
+      - --port
+      - "8000"
+      - --gpu-memory-utilization
+      - ${QWEN_GPU_MEM_UTIL}
+      - --max-model-len
+      - ${QWEN_MAX_MODEL_LEN}
+      - --dtype
+      - auto
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids:
+                - "${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=Qwen3.5-9B VL (FP8)
+      - homepage.icon=mdi-image-search
+      - homepage.description=Qwen3.5-9B vision-language (FP8) via vLLM (ana-ml2)
+      - homepage.href=http://10.250.50.54:${QWEN_PORT}/docs
+
+networks:
+  tnet:
+    name: traefik-net
+    external: true