diff --git a/stacks/litellm/conf/config.yaml b/stacks/litellm/conf/config.yaml
index 77b1206..48a19f7 100644
--- a/stacks/litellm/conf/config.yaml
+++ b/stacks/litellm/conf/config.yaml
@@ -67,6 +67,19 @@ model_list:
     model_info:
       mode: chat
 
+  # --- Mistral Small 4 (official NVFP4) — creative-writing / general text. 119B
+  #     MoE (6.5B active), vLLM on ana-ml2 GPU 0 (dedicated 96 GB Blackwell), :8010.
+  #     Explicit entry shadows the "*" wildcard. TEXT-ONLY for now — vLLM 0.23.0's
+  #     Mistral multimodal processor crashes at startup (loaded with image/video
+  #     limit 0); vision returns when vLLM patches it. Deployed 2026-06-15. ---
+  - model_name: mistral-small-4
+    litellm_params:
+      model: hosted_vllm/mistral-small-4
+      api_base: http://10.250.50.54:8010/v1
+      api_key: os.environ/VLLM_API_KEY
+    model_info:
+      mode: chat
+
   # --- Qwen3 embeddings ---
   - model_name: qwen3-embedding
     litellm_params:
diff --git a/stacks/mistral-small-4/.env.example b/stacks/mistral-small-4/.env.example
new file mode 100644
index 0000000..5b1d72a
--- /dev/null
+++ b/stacks/mistral-small-4/.env.example
@@ -0,0 +1,32 @@
+# Mistral Small 4 (official NVFP4) on ana-ml2 GPU 0 — copy to .env on the host.
+# Real .env lives on ana-ml2 at /opt/docker/compose/mistral-small-4/.env (gitignored).
+#
+# See compose.yaml header for the NVFP4/TP=1/MLA rationale and the vLLM>=0.20 floor.
+
+# PINNED by digest, not :latest — this model is version-sensitive (needs vLLM
+# >= 0.20 for day-0 support; the related nvidia-ModelOpt NVFP4 MoE path broke on
+# 0.19.1/0.22.0). Pin protects against a :latest regression. This digest = vLLM
+# 0.23.0, the version validated to load this checkpoint. Bump deliberately.
+MISTRAL_IMAGE=vllm/vllm-openai@sha256:6d8429e38e3747723ca07ee1b17972e09bb9c51c4032b266f24fb1cc3b22ed8f
+
+MISTRAL_CONTAINER_NAME=vllm-mistral4
+MISTRAL_MODEL=mistralai/Mistral-Small-4-119B-2603-NVFP4
+MISTRAL_PORT=8010
+
+# GPU 0 = the free 96 GB Blackwell card, dedicated single-tenant to this model
+# (74.4 GB weights leave no room to co-tenant). GPU 1 holds qwen36 + granite +
+# the embed/rerank/reward trio.
+MISTRAL_GPU_ID=0
+
+# util 0.93 (~89 GB budget) — 74.4 GB weights + ~5 GB CUDA/graph leaves ~10 GB
+# KV. MLA keeps KV compressed so 131072 ctx fits; raise toward native 256K only
+# after measuring real KV headroom. Dedicated card, so 0.93 is safe.
+MISTRAL_GPU_MEM_UTIL=0.93
+MISTRAL_MAX_MODEL_LEN=131072
+# Single-card KV is tighter than the official TP=2 setup → cap concurrency at 64
+# (official used 128 across two cards).
+MISTRAL_MAX_NUM_SEQS=64
+
+# Optional — model is ungated (Apache-2.0), no token needed.
+HF_TOKEN=
+API_KEY=
diff --git a/stacks/mistral-small-4/compose.yaml b/stacks/mistral-small-4/compose.yaml
new file mode 100644
index 0000000..58c125c
--- /dev/null
+++ b/stacks/mistral-small-4/compose.yaml
@@ -0,0 +1,116 @@
+# mistral-small-4 — Mistral-Small-4-119B-2603 (official NVFP4) on ana-ml2 GPU 0.
+#
+# Mistral Small 4 is a 119B-total / 6.5B-active MoE (128 experts, 4 active),
+# 256K context, multimodal, Apache-2.0 (released 2026-03). This serves the
+# OFFICIAL NVFP4 checkpoint (mistralai/Mistral-Small-4-119B-2603-NVFP4) — 74.4 GB
+# of compressed-tensors (llm-compressor, a vLLM + Red Hat collaboration, day-0
+# vLLM support). It is the GPU-0 tenant (the slot formerly reserved for a
+# creative-writing pick — operator reassigned 2026-06-15; tune-for-creative-
+# writing comes after base-characteristic probing).
+#
+# WHY NVFP4 (not FP8/bf16): on a SINGLE 96 GB card, NVFP4 (74.4 GB weights) is
+# the only variant that fits at TP=1 — FP8 (~119 GB) and bf16 (~238 GB) need both
+# GPUs. The card is Blackwell (sm_120) with FP4 tensor cores, so NVFP4 gets a real
+# speedup, not just a VRAM save. NOTE: this is the COMPRESSED-TENSORS NVFP4 path
+# (vendor-shipped, vLLM-tested) — distinct from the nvidia-ModelOpt NVFP4 MoE
+# loader that broke on Qwen3.6 (#44081); different code path, day-0 supported.
+#
+# WHY TP=1 here: Mistral's official card uses --tensor-parallel-size 2 (their
+# reference 80 GB cards can't fit 74.4 GB + context on one). The 96 GB Blackwell
+# flips that to single-card: 74.4 GB weights + ~5 GB overhead leaves ~17 GB for
+# KV. Mistral Small 4 uses MLA attention (TRITON_MLA) so KV is compressed/cheap —
+# big context stays affordable even on a constrained KV pool. max-model-len is
+# capped to 131072 on first bring-up (raise toward the native 256K once real KV
+# headroom is measured).
+#
+# vLLM FLOOR: needs >= 0.20 (Mistral Small 4 day-0 support); validated on 0.23.0.
+# Do NOT reuse the qwen36-vl 0.19.1 image — it predates this model.
+#
+# Serve flags mirror Mistral's official command (cited in README), adapted for
+# single-card: TP 2->1, util 0.8->0.93, max-len 262144->131072, max-num-seqs
+# 128->64. All tunables live in .env — edit that, not this file.
+
+name: mistral-small-4
+
+services:
+  vllm-mistral4:
+    image: ${MISTRAL_IMAGE}
+    container_name: ${MISTRAL_CONTAINER_NAME}
+    restart: unless-stopped
+    ipc: host
+    ports:
+      - "${MISTRAL_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:
+      - ${MISTRAL_MODEL}
+      # Pre-quantized NVFP4 (compressed-tensors) — vLLM auto-detects the quant;
+      # no --quantization flag.
+      - --served-model-name
+      - mistral-small-4
+      - --host
+      - 0.0.0.0
+      - --port
+      - "8000"
+      - --tensor-parallel-size
+      - "1"
+      - --gpu-memory-utilization
+      - ${MISTRAL_GPU_MEM_UTIL}
+      - --max-model-len
+      - ${MISTRAL_MAX_MODEL_LEN}
+      # MLA attention backend (DeepSeek-style latent KV → compressed, cheap KV).
+      - --attention-backend
+      - TRITON_MLA
+      # Mistral tool-calling + configurable reasoning (per the official card).
+      - --tool-call-parser
+      - mistral
+      - --enable-auto-tool-choice
+      - --reasoning-parser
+      - mistral
+      - --max-num-seqs
+      - ${MISTRAL_MAX_NUM_SEQS}
+      # TEXT-ONLY (2026-06-15): vLLM 0.23.0's Mistral multimodal processor crashes
+      # at startup dummy-image profiling — `MistralCommonImageProcessor has no
+      # attribute fetch_images` (vLLM↔mistral_common incompat; same class hit
+      # Mistral-3.1/Devstral/Magistral). Setting image/video limit to 0 skips the
+      # vision profiling so the model loads text-only — which is all the creative-
+      # writing use needs. REMOVE this flag to restore vision once vLLM patches the
+      # Mistral mm path (track: the model is natively multimodal).
+      - --limit-mm-per-prompt
+      - '{"image":0,"video":0}'
+      - --dtype
+      - auto
+      - --enable-prefix-caching
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids:
+                - "${MISTRAL_GPU_ID}"
+              capabilities:
+                - gpu
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 600s
+    networks:
+      - tnet
+    labels:
+      - homepage.group=AI Systems
+      - homepage.name=Mistral Small 4 (NVFP4)
+      - homepage.icon=mdi-creation
+      - homepage.description=Mistral-Small-4-119B-2603 MoE (NVFP4) via vLLM (ana-ml2 GPU 0)
+      - homepage.href=http://10.250.50.54:${MISTRAL_PORT}/docs
+
+networks:
+  tnet:
+    name: traefik-net
+    external: true