diff --git a/.env.example b/.env.example
index 67b57c4..197d72b 100644
--- a/.env.example
+++ b/.env.example
@@ -36,12 +36,20 @@ WHISPER_DEVICE=cuda
 WHISPER_COMPUTE_TYPE=float16
 # Optional explicit Piper voice model (.onnx). If empty, the jarvis default is used.
 TTS_PIPER_MODEL_PATH=
-# TTS engine: "edge" (default) uses Microsoft Edge TTS, a natural Korean neural
-# voice. Set to "piper" for the offline English voice. NOTE: edge is ONLINE —
-# reply text is sent to Microsoft's servers and needs internet.
-TTS_ENGINE=edge
-# Edge voice + speaking rate. Rate is a percentage (+45% ≈ 1.45×). Korean voices:
-# ko-KR-HyunsuMultilingualNeural (M), ko-KR-InJoonNeural (M), ko-KR-SunHiNeural (F).
+# TTS engine:
+#   melo  (default) GPU MeloTTS — offline Korean voice, privacy-first (text never
+#         leaves the host), synthesises on the GPU via the melo-worker. Needs a
+#         GPU (MELO_DEVICE=cuda); falls back to CPU (slow) with MELO_DEVICE=cpu.
+#   edge  Microsoft Edge TTS — natural Korean neural voice, but ONLINE: reply
+#         text is sent to Microsoft's servers and needs internet.
+#   piper offline English voice (fallback).
+TTS_ENGINE=melo
+# GPU MeloTTS knobs (used when TTS_ENGINE=melo).
+MELO_DEVICE=cuda
+MELO_SPEED=1.5
+# Edge voice + speaking rate (used when TTS_ENGINE=edge). Rate is a percentage
+# (+45% ≈ 1.45×). Korean voices: ko-KR-HyunsuMultilingualNeural (M),
+# ko-KR-InJoonNeural (M), ko-KR-SunHiNeural (F).
 EDGE_TTS_VOICE=ko-KR-HyunsuMultilingualNeural
 EDGE_TTS_RATE=+45%
 # Neural-only by default: if synthesis fails the bridge returns no audio rather
diff --git a/Dockerfile b/Dockerfile
index fd19fdb..cba935f 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -65,10 +65,19 @@ RUN ls -d /opt/venv/lib/python*/site-packages/nvidia/cublas/lib \
         > /etc/ld.so.conf.d/nvidia-cu12.conf 2>/dev/null \
     && /sbin/ldconfig || true
 
-# --- Korean voice: Microsoft Edge TTS (online neural). No model is baked — the
-#     `edge-tts` pip package (in requirements-bridge.txt) calls the MS service at
-#     runtime and the bridge transcodes the MP3 to PCM16 with ffmpeg. No heavy
-#     TTS build layer is needed. ---
+# --- Korean voice: GPU MeloTTS (offline, privacy-first) in a separate /opt/melo
+#     py3.11 venv; see docker/setup-melo.sh. Heavy layer (torch cu128 GPU +
+#     transformers + MeCab + baked KR model), placed before the app COPY so it
+#     stays cached across source-only changes. Runtime device is MELO_DEVICE=cuda
+#     (compose), so TTS synthesises on the GPU next to Ollama + Whisper. Started
+#     only in full/bot/brain roles (see docker/supervisord.conf). The offline
+#     Edge TTS (`edge-tts`, in requirements-bridge.txt) remains available as an
+#     opt-in fallback via TTS_ENGINE=edge, but it is ONLINE (text leaves the
+#     host), so MeloTTS is the default. ---
+COPY docker/setup-melo.sh /app/docker/setup-melo.sh
+# Strip CR first: a Windows (autocrlf) checkout yields CRLF, which makes bash
+# read `set -euxo pipefail\r` and abort. Idempotent on an LF tree.
+RUN sed -i 's/\r$//' /app/docker/setup-melo.sh && bash /app/docker/setup-melo.sh
 
 # --- Human input + window management for the on-screen Chrome control tool.
 #     xdotool injects real X pointer/keyboard events (visible cursor,
diff --git a/docker-compose.yml b/docker-compose.yml
index 7ab9daa..859846c 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -87,11 +87,17 @@ services:
       WHISPER_DEVICE: ${WHISPER_DEVICE:-cuda}
       WHISPER_COMPUTE_TYPE: ${WHISPER_COMPUTE_TYPE:-float16}
       # TTS engine. Rendered into /app/config/jarvis.json via envsubst (the
-      # bridge reads that JSON BEFORE the env, so it must carry the real engine,
-      # not a hardcoded one — otherwise Korean text is read by the English Piper
-      # voice). Default edge; .env can override (e.g. piper for offline).
-      TTS_ENGINE: ${TTS_ENGINE:-edge}
-      # Edge TTS voice + rate (the chosen natural Korean voice). NOTE: edge is an
+      # bridge reads that JSON BEFORE the env, so it must carry the real engine).
+      # Default melo: GPU MeloTTS (offline, privacy-first) via the melo-worker.
+      # Override with TTS_ENGINE=edge for the online Microsoft neural voice (text
+      # leaves the host) or piper for the offline English voice.
+      TTS_ENGINE: ${TTS_ENGINE:-melo}
+      # GPU MeloTTS worker knobs. MELO_DEVICE=cuda runs synth on the GPU (next to
+      # Ollama + Whisper); cpu is the no-GPU fallback. MELO_SPEED must always be
+      # set — supervisord's melo-worker expands %(ENV_MELO_SPEED)s at startup.
+      MELO_DEVICE: ${MELO_DEVICE:-cuda}
+      MELO_SPEED: ${MELO_SPEED:-1.5}
+      # Edge TTS voice + rate (used only when TTS_ENGINE=edge). NOTE: edge is an
       # ONLINE engine — reply text is sent to Microsoft and needs internet.
       EDGE_TTS_VOICE: ${EDGE_TTS_VOICE:-ko-KR-HyunsuMultilingualNeural}
       EDGE_TTS_RATE: ${EDGE_TTS_RATE:-+45%}
diff --git a/docker/supervisord.conf b/docker/supervisord.conf
index dab8639..766b9f5 100644
--- a/docker/supervisord.conf
+++ b/docker/supervisord.conf
@@ -49,8 +49,27 @@ stdout_logfile_maxbytes=0
 stderr_logfile=/dev/stderr
 stderr_logfile_maxbytes=0
 
-# (No TTS worker program: the default Edge TTS engine synthesises in-process in
-# the bridge via the `edge-tts` package — no warm model/worker is needed.)
+[program:melo-worker]
+; Warm GPU MeloTTS Korean voice (offline) in its own py3.11 venv (/opt/melo). The
+; bridge's synthesize() POSTs here when TTS_ENGINE=melo. Runs wherever the bridge
+; runs (full/bot/brain) so a GPU brain host serves TTS on the GPU. If TTS_ENGINE
+; =edge the bridge synthesises in-process and never calls this worker.
+command=/app/docker/run-if-role.sh full,bot,brain /opt/melo/bin/python /app/bridge/melo_worker.py
+directory=/app
+; HF_HOME points at the dedicated, image-baked melo cache (warmed in
+; setup-melo.sh). The whisper_cache volume is mounted over /root/.cache/huggingface,
+; so without this the pre-cached BERT + KR checkpoint would be shadowed and
+; re-downloaded (and would fail offline). HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE
+; force pure-cache reads. MELO_DEVICE/MELO_SPEED inherit from the container env
+; (compose sets both with defaults cuda / 1.5), so MELO_SPEED must always be set
+; in the env or this %(ENV_x)s expansion fails at startup (compose guarantees it).
+environment=MELO_LANGUAGE="KR",MELO_SPEED="%(ENV_MELO_SPEED)s",MELO_DEVICE="%(ENV_MELO_DEVICE)s",MELO_WORKER_HOST="127.0.0.1",MELO_WORKER_PORT="8770",HF_HOME="/opt/melo-cache",HF_HUB_OFFLINE="1",TRANSFORMERS_OFFLINE="1"
+priority=280
+autorestart=true
+stdout_logfile=/dev/stdout
+stdout_logfile_maxbytes=0
+stderr_logfile=/dev/stderr
+stderr_logfile_maxbytes=0
 
 [program:bridge]
 command=/app/docker/run-if-role.sh full,bot,brain /opt/venv/bin/python -m bridge.server
diff --git a/docs/DEPLOY.md b/docs/DEPLOY.md
index d874cbf..535dad3 100644
--- a/docs/DEPLOY.md
+++ b/docs/DEPLOY.md
@@ -100,13 +100,16 @@ BRIDGE_HOST=0.0.0.0                            # bridge listens on the LAN
 SETTINGS_PUBLISH_BIND=0.0.0.0                  # publish bridge port 8765 to the LAN
 OLLAMA_BASE_URL=http://192.168.10.9:11434      # Ollama on this host (bind 0.0.0.0)
 OLLAMA_CHAT_MODEL=qwen2.5:7b
-WHISPER_DEVICE=cuda
+WHISPER_DEVICE=cuda                            # STT on the GPU
 WHISPER_COMPUTE_TYPE=float16
+TTS_ENGINE=melo                                # GPU MeloTTS (offline, privacy-first)
+MELO_DEVICE=cuda                               # TTS on the GPU (melo-worker)
 BROWSER_CONTROL_URL=http://192.168.10.5:8777   # drive the app host's Chrome
 COMPOSE_FILE=docker-compose.yml:docker-compose.gpu-linux.yml   # GPU (":" on Linux)
 
-# Ollama runs on this host (systemd or a container) listening on 0.0.0.0:11434.
-docker compose up -d javis      # brain bridge only (no bot, no desktop)
+# STT (Whisper), LLM (Ollama) and TTS (MeloTTS) all run on this host's GPU.
+# Ollama listens on 0.0.0.0:11434 (systemd or a container).
+docker compose up -d javis      # brain bridge + melo-worker only (no bot, no desktop)
 ```
 
 ### App host (.5 — bot + browser, no GPU)