diff --git a/.env.example b/.env.example
index cb8ba9e..424275d 100644
--- a/.env.example
+++ b/.env.example
@@ -21,7 +21,7 @@ MEDIUM_CONTEXT=160000
 LARGE_CONTEXT=192000
 ULTRA_CONTEXT=262144
 EXPERIMENTAL_CONTEXT=76800
-FAST_GPU_DEVICES=0
+FAST_GPU_DEVICES=0,1
 MEDIUM_GPU_DEVICES=0,1
 MEDIUM_TENSOR_SPLIT=90,10
 LARGE_GPU_DEVICES=0,1
diff --git a/README.md b/README.md
index a7612f2..037f443 100644
--- a/README.md
+++ b/README.md
@@ -102,3 +102,8 @@ router/                                 OpenAI-kompatibler Profile Router
 Die Profilwerte wurden auf RTX 5080 und RTX 3060 vermessen und bilden die
 verbindliche Standardmatrix. Neue Varianten ersetzen sie erst nach demselben
 Vergleichstest und einer dokumentierten Entscheidung.
+
+Der integrierte Vision-Projektor der Profile Fast, Medium und Large läuft
+gezielt auf der RTX 3060. Das hält den knappen VRAM der RTX 5080 für Modell und
+Kontext frei und beschleunigte den dokumentierten synthetischen Vision-Test
+gegenüber CPU-Vision um etwa den Faktor 8,5 bei der Gesamtzeit.
diff --git a/compose.yaml b/compose.yaml
index 94c0d73..396e201 100644
--- a/compose.yaml
+++ b/compose.yaml
@@ -33,14 +33,18 @@ services:
     labels:
       com.mike-ai.llama-profile: fast
     environment:
-      NVIDIA_VISIBLE_DEVICES: ${FAST_GPU_DEVICES:-0}
+      NVIDIA_VISIBLE_DEVICES: ${FAST_GPU_DEVICES:-0,1}
       NVIDIA_DRIVER_CAPABILITIES: compute,utility
+      # CUDA0 remains the exclusive text-model device. The projector is kept
+      # on the secondary card so vision does not consume the 5080 context
+      # budget.
+      MTMD_BACKEND_DEVICE: CUDA1
     command:
       - --model
       - "/models/${FAST_MODEL_FILE:?FAST_MODEL_FILE is required}"
       - --mmproj
       - "/models/${VISION_PROJECTOR_FILE:?VISION_PROJECTOR_FILE is required}"
-      - --no-mmproj-offload
+      - --mmproj-offload
       - --alias
       - qwen-fast
       - --ctx-size
@@ -102,12 +106,15 @@ services:
     environment:
       NVIDIA_VISIBLE_DEVICES: ${MEDIUM_GPU_DEVICES:-0,1}
       NVIDIA_DRIVER_CAPABILITIES: compute,utility
+      # Keep the language model split unchanged while placing the complete
+      # multimodal projector on the secondary RTX 3060.
+      MTMD_BACKEND_DEVICE: CUDA1
     command:
       - --model
       - "/models/${MEDIUM_MODEL_FILE:?MEDIUM_MODEL_FILE is required}"
       - --mmproj
       - "/models/${VISION_PROJECTOR_FILE:?VISION_PROJECTOR_FILE is required}"
-      - --no-mmproj-offload
+      - --mmproj-offload
       - --alias
       - qwen-medium
       - --ctx-size
@@ -173,12 +180,13 @@ services:
     environment:
       NVIDIA_VISIBLE_DEVICES: ${LARGE_GPU_DEVICES:-0,1}
       NVIDIA_DRIVER_CAPABILITIES: compute,utility
+      MTMD_BACKEND_DEVICE: CUDA1
     command:
       - --model
       - "/models/${LARGE_MODEL_FILE:?LARGE_MODEL_FILE is required}"
       - --mmproj
       - "/models/${VISION_PROJECTOR_FILE:?VISION_PROJECTOR_FILE is required}"
-      - --no-mmproj-offload
+      - --mmproj-offload
       - --alias
       - qwen-large
       - --ctx-size
diff --git a/dev/benchmark_vision.py b/dev/benchmark_vision.py
new file mode 100644
index 0000000..8467966
--- /dev/null
+++ b/dev/benchmark_vision.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""Run a private, deterministic llama.cpp vision latency smoke test."""
+
+from __future__ import annotations
+
+import argparse
+import base64
+import json
+import struct
+import time
+import urllib.request
+import zlib
+
+
+def chunk(kind: bytes, payload: bytes) -> bytes:
+    return (
+        struct.pack(">I", len(payload))
+        + kind
+        + payload
+        + struct.pack(">I", zlib.crc32(kind + payload) & 0xFFFFFFFF)
+    )
+
+
+def synthetic_png(width: int = 1024, height: int = 768) -> bytes:
+    rows = []
+    for y in range(height):
+        row = bytearray([0])
+        for x in range(width):
+            row.extend(((x * 255) // width, (y * 255) // height, ((x // 64 + y // 64) % 2) * 210))
+        rows.append(bytes(row))
+    payload = zlib.compress(b"".join(rows), level=6)
+    return (
+        b"\x89PNG\r\n\x1a\n"
+        + chunk(b"IHDR", struct.pack(">IIBBBBB", width, height, 8, 2, 0, 0, 0))
+        + chunk(b"IDAT", payload)
+        + chunk(b"IEND", b"")
+    )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--url", required=True)
+    parser.add_argument("--model", default="qwen-medium")
+    args = parser.parse_args()
+
+    image = base64.b64encode(synthetic_png()).decode("ascii")
+    body = {
+        "model": args.model,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/png;base64,{image}"},
+                    },
+                    {
+                        "type": "text",
+                        "text": "Beschreibe dieses synthetische Testbild knapp auf Deutsch.",
+                    },
+                ],
+            }
+        ],
+        "temperature": 0,
+        "max_tokens": 80,
+        "cache_prompt": False,
+    }
+    request = urllib.request.Request(
+        args.url.rstrip("/") + "/v1/chat/completions",
+        data=json.dumps(body).encode("utf-8"),
+        headers={"Content-Type": "application/json"},
+    )
+    started = time.perf_counter()
+    with urllib.request.urlopen(request, timeout=600) as response:
+        result = json.load(response)
+    elapsed = time.perf_counter() - started
+    print(
+        json.dumps(
+            {
+                "wall_seconds": round(elapsed, 3),
+                "usage": result.get("usage"),
+                "timings": result.get("timings"),
+                "answer": result["choices"][0]["message"].get("content", "")[:240],
+            },
+            ensure_ascii=False,
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
index 148461e..a4343d8 100644
--- a/docs/ARCHITECTURE.md
+++ b/docs/ARCHITECTURE.md
@@ -68,6 +68,14 @@ halten.
 Diese Matrix wurde auf RTX 5080 und RTX 3060 gemessen und ist bis zu einer
 bewussten Neubewertung der verbindliche Produktionsstandard.
 
+Bei Fast, Medium und Large bleibt das Sprachmodell wie in der Tabelle
+verteilt, während `MTMD_BACKEND_DEVICE=CUDA1` den vollständigen
+Multimodal-Projektor auf die RTX 3060 legt. Ein reproduzierbarer Test mit einem
+synthetischen Bild (1024×768, 835 Eingabetoken) senkte die Bild-/Promptzeit im
+Medium-Profil von 23,17 auf 1,68 Sekunden und die gesamte Anfrage von 24,96
+auf 2,94 Sekunden. Der Projektor belegte dabei rund 1,1 GiB zusätzlichen VRAM
+auf der 3060. Ultra bleibt für maximalen Kontext bewusst text-only.
+
 ## Netzwerk
 
 - Docker-Netze liegen ausschließlich unter `172.30.0.0/16`.
diff --git a/install.sh b/install.sh
index bf7729c..258c3fa 100755
--- a/install.sh
+++ b/install.sh
@@ -218,7 +218,7 @@ MEDIUM_CONTEXT=${MEDIUM_CONTEXT:-160000}
 LARGE_CONTEXT=${LARGE_CONTEXT:-192000}
 ULTRA_CONTEXT=${ULTRA_CONTEXT:-262144}
 EXPERIMENTAL_CONTEXT=${EXPERIMENTAL_CONTEXT:-76800}
-FAST_GPU_DEVICES=${TEXT_GPU_DEVICES:-0}
+FAST_GPU_DEVICES=${TEXT_GPU_DEVICES:-0}${SECONDARY_GPU_DEVICES:+,$SECONDARY_GPU_DEVICES}
 MEDIUM_GPU_DEVICES=${TEXT_GPU_DEVICES:-0}${SECONDARY_GPU_DEVICES:+,$SECONDARY_GPU_DEVICES}
 MEDIUM_TENSOR_SPLIT=${MEDIUM_TENSOR_SPLIT:-90,10}
 LARGE_GPU_DEVICES=${TEXT_GPU_DEVICES:-0}${SECONDARY_GPU_DEVICES:+,$SECONDARY_GPU_DEVICES}