How to Deploy Coqui TTS on a Dedicated GPU Server

Why Deploy Coqui TTS on Dedicated Hardware

Coqui TTS is one of the most capable open-source text-to-speech frameworks available, supporting dozens of languages and offering state-of-the-art voice cloning through its XTTS v2 model. Running Coqui TTS on a dedicated GPU server delivers the low latency needed for real-time voice synthesis in chatbots, IVR systems, audiobook production, and accessibility tools.

GigaGPU’s Coqui TTS hosting provides GPU infrastructure optimised for speech synthesis workloads. Unlike CPU-based TTS that can take several seconds per sentence, GPU acceleration generates speech in real time or faster, making it practical for interactive applications. This guide walks through installation, model setup, API configuration, and voice cloning with XTTS. For a broader look at GPU choices for voice AI, read our best GPU for TTS and voice AI guide.

GPU VRAM Requirements for Coqui TTS

TTS models are relatively lightweight compared to large language models, but VRAM requirements grow with model complexity and batch size.

Even entry-level GPUs handle single-stream TTS well, but a dedicated server lets you scale to many concurrent streams. For multi-model deployments combining TTS with an LLM, see GigaGPU’s speech model hosting options.

Preparing Your GPU Server

Update your system and verify GPU access:

sudo apt update && sudo apt upgrade -y
sudo apt install -y python3 python3-pip python3-venv git ffmpeg espeak-ng
nvidia-smi

The espeak-ng package provides phoneme conversion used by several TTS models, and ffmpeg handles audio format conversion.

Create a virtual environment:

python3 -m venv ~/tts-env
source ~/tts-env/bin/activate
pip install --upgrade pip

Install PyTorch with CUDA support. See our PyTorch GPU installation guide for version-specific instructions:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Installing Coqui TTS

Install the TTS package from PyPI:

pip install TTS

List available models to see what is ready to download:

tts --list_models

Generate a quick test with the default VITS model:

tts --text "Welcome to GigaGPU's dedicated GPU hosting." \
    --model_name tts_models/en/ljspeech/vits \
    --out_path output.wav

Verify the output plays correctly:

ffplay output.wav

Launching the TTS Server

Coqui TTS includes a built-in HTTP server for API access. Start it with XTTS v2:

tts-server \
  --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
  --host 0.0.0.0 \
  --port 5002 \
  --use_cuda true

The server provides a web UI at http://your-server-ip:5002 and an API endpoint. Test it with curl:

curl -X GET "http://localhost:5002/api/tts?text=Hello%20from%20a%20dedicated%20GPU%20server&speaker_id=0&language_id=en" \
  --output test_output.wav

For production API deployments, explore GigaGPU’s API hosting with load balancing and SSL termination.

Voice Cloning with XTTS

XTTS v2 supports zero-shot voice cloning from a short reference audio clip. Prepare a clean 6-15 second WAV file of the target voice, then run:

tts --text "This is a cloned voice speaking from a dedicated GPU server." \
    --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
    --speaker_wav reference_voice.wav \
    --language_id en \
    --out_path cloned_output.wav \
    --use_cuda true

For the API server, send the reference audio as a file upload:

curl -X POST "http://localhost:5002/api/tts" \
  -F "text=Voice cloning test on GPU hardware." \
  -F "speaker_wav=@reference_voice.wav" \
  -F "language=en" \
  --output cloned_api_output.wav

XTTS v2 supports 17 languages including English, Spanish, French, German, Chinese, Japanese, and Arabic, making it ideal for multilingual voice applications.

Production Tips and Next Steps

Optimise your Coqui TTS deployment for production:

• Use FP16 inference — Halves VRAM usage with negligible quality impact. Pass --half or set torch_dtype=torch.float16 in code.
• Enable streaming — XTTS v2 supports chunked audio streaming for lower time-to-first-byte in real-time applications.
• Combine with Whisper — Build a full speech pipeline by pairing Coqui TTS with OpenAI Whisper for transcription. See GigaGPU’s Whisper hosting and our Whisper RTF by GPU benchmark.
• Run behind a reverse proxy — Use Nginx with SSL for secure external access to the TTS API.
• Scale with multiple models — Load different voice models on separate GPU devices for concurrent multi-voice synthesis.

If you are building a complete voice AI stack, explore our guide on building an AI chatbot server which covers integrating TTS with an LLM backend. Browse more deployment walkthroughs in our model guides category.