Text-to-Speech (TTS) Hosting, Hosted AI Voice Generator

Discover the power of our Text-to-Speech hosting service. Create lifelike audio with our
AI voice generator and enhance your projects effortlessly.

  • Windows or Linux OS
  • Full Root/Admin Access
  • Support RDP/SSH Access
  • 24/7 technical support from real humans
  • Dedicated high-performance GPU (no sharing)

Choose Your AI Voice Generator Hosting Plans

Infotronics Integrators (I) Pvt. Ltd offers best budget GPU servers for text to speech online. Cost-effective hosted text reader is ideal for hosting your own TTS service.

Express GPU Dedicated Server - P1000


  • 32GB RAM
  • GPU: Nvidia Quadro P1000
  • Eight-Core Xeon E5-2690
  • (8 Cores & 16 Threads)
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Pascal
  • CUDA Cores: 640
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 1.894
         TFLOPS



    • Basic GPU Dedicated Server - T1000


  • 64GB RAM
  • GPU: Nvidia Quadro T1000
  • Eight-Core Xeon E5-2690
  • (8 Cores & 16 Threads)
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Turing
  • CUDA Cores: 896
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 2.5
        TFLOPS



    • Basic GPU Dedicated Server - GTX 1650


  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1650
  • Eight-Core Xeon E5-2667v3
  • (8 Cores & 16 Threads)
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Turing
  • CUDA Cores: 896
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 3.0
         TFLOPS

    • Basic GPU Dedicated Server - GTX 1660


  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1660
  • Dual 10-Core Xeon E5-2660v2
  • (20 Cores & 40 Threads)
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Turing
  • CUDA Cores: 1408
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 5.0
         TFLOPS

    • Professional GPU Dedicated Server - RTX 2060

  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 10-Core E5-2660v2
  • (20 Cores & 40 Threads)
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps

    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ampere
  • CUDA Cores: 1920
  • Tensor Cores: 240
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 6.5
         TFLOPS



    • Advanced GPU Dedicated Server - RTX 3060 Ti

  • 128GB RAM
  • GPU: GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • (24 Cores & 48 Threads)
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps

    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ampere
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2
        TFLOPS



    • Basic GPU Dedicated Server - RTX 4060

  • 64GB RAM
  • GPU: Nvidia GeForce RTX 4060
  • Eight-Core E5-2690
  • (8 Cores & 16 Threads)
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps

    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 3072
  • Tensor Cores: 96
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 15.11
        TFLOPS

    • Enterprise GPU Dedicated Server - RTX 4090

  • 256GB RAM
  • GPU: GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • (36 Cores & 76 Threads)
  • 240GB SSD + 2TB NVMe + 8TB
        SATA
  • 100Mbps-1Gbps

    • OS: Windows / Linux
    Single GPU Specifications

  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6
        TFLOPS


    • Multi-GPU
    Dedicated Server- 2xRTX 5090


  • 256GB RAM
  • GPU: 2 x GeForce RTX 5090
  • Dual Gold 6148
  • (40 Cores & 80 Threads)
  • 240GB SSD + 2TB NVMe + 8TB
          SATA
  • 1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 20,480
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7
        TFLOPS

    • Enterprise GPU Dedicated Server - A100


  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • (36 Cores & 72 Threads)
  • 240GB SSD + 2TB NVMe + 8TB
         SATA
  • 100Mbps-1Gbps


    • OS: Windows / Linux
    Single GPU Specifications:

  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5
         TFLOPS

    • Top Open Source Speech Recognition Models

    Here’s a curated list of the Top Open Source Text-to-Speech (TTS) Models as of 2025, selected for their voice quality, community adoption, and ease of integration.

    🏆 Top Open Source TTS Models (2025 Edition)

    Model Key Features Language Support Voice Cloning Inference Speed License GitHub
    ChatTTS High-quality, real-time TTS optimized for chatbot speech 🇨🇳 Chinese, 🇺🇸 English Planned ⚡ Fast Apache 2.0
    OpenVoice (MyShell) Multilingual, real-time cross-lingual voice cloning 🌐 Multilingual ✅ Yes (few sec sample) ⚡ Fast MIT
    XTTS v3 (Coqui) Zero-shot cloning, Hugging Face compatible, production-ready 🌐 Multilingual ✅ Yes ⚡ Fast Apache 2.0
    Tortoise TTS Extremely natural, expressive, few-shot cloning 🇺🇸 English (mainly) ✅ Yes 🐢 Slow Apache 2.0
    Bark (Suno) Audio + emotion + sound FX generation 🌐 Multilingual ❌ No 🚀 Medium MIT
    VITS / VITS2 GAN + variational inference, customizable 🌐 Multilingual ⚠️ Limited ⚡ Fast MIT
    ESPnet-TTS Research-friendly toolkit with multiple TTS backends 🌐 Multilingual ⚠️ Optional 🚀 Medium Apache 2.0
    Mozilla TTS (Legacy) Early open-source model, deprecated but stable 🌐 Multiple ⚠️ Basic 🚀 Medium MPL 2.0

    🏅 Best by Category

    Use Case Recommended Model
    Real-Time Chatbot Voice ChatTTS, OpenVoice
    Voice Cloning Tortoise, XTTS, OpenVoice
    Multilingual Support OpenVoice, XTTS, Bark
    Expressive/Creative Audio Bark, Tortoise
    Lightweight Deployment VITS2, ChatTTS
    Research/Training ESPnet, Coqui TTS

    🌟 Notes

    Why Choose our GPU Servers for TTS Hosting?

    Infotronics Integrators (I) Pvt. enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.

    Intel Xeon CPU

    Wide GPU Selection

    Infotronics Integrators (I) Pvt. Ltd provides a diverse range of NVIDIA GPUs, including models like RTX 3060 Ti, RTX 4090, A100, and V100, catering to various performance needs for Whisper's different model sizes.

    SSD-Based Drives

    Premium Hardware

    Our GPU dedicated servers and VPS are equipped with high-quality NVIDIA graphics cards, efficient Intel CPUs, pure SSD storage, and renowned memory brands such as Samsung and Hynix.

    Full Root/Admin Access

    Dedicated Resources

    Each server comes with dedicated GPU cards, ensuring consistent performance without resource contention.

    99.9% Uptime Guarantee

    99.9% Uptime Guarantee

    With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for deep learning and networks.

    Dedicated IP

    Secure & Reliable

    Enjoy 99.9% uptime, daily backups, and enterprise-grade security. Your data—and your art—is safe with us.


    24/7/365 Technical Support

    24/7/365 Free Expert Support

    Our dedicated support team is comprised of experienced professionals. From initial deployment to ongoing maintenance and troubleshooting, we're here to provide the assistance you need, whenever you need it, without extra fee.

    How to Install AI Voice Generator ChatTTS

    Here’s a step-by-step guide to installing and running ChatTTS, the open-source AI voice generator that delivers high-quality, natural speech in English and Mandarin Chinese.




    Order and login a GPU server



    Clone the Repository and Create a Virtual Environment



    Install Dependencies and Required Libraries




    Running a Voice Generation Example



    FAQs of Text to Speech Hosting

    The most commonly asked questions about Whisper Speech to Text hosting service below.

    What is Text-to-Speech (TTS)?
    Text-to-Speech (TTS) is a type of assistive and generative AI technology that converts written text into spoken voice output using synthetic speech.
    Text-to-Speech (TTS) is widely used in virtual assistants, screen readers, customer service systems, and content creation tools like audiobooks and AI voiceovers. TTS enhances accessibility for the visually impaired and supports multitasking by reading messages, articles, or directions aloud. Emerging uses include real-time dubbing, voice cloning, and AI-powered character voices in games and the metaverse.
    Use GPU load balancing (multiple worker nodes), Add caching for repeated prompts, Queue requests with Redis + Celery, Deploy behind Nginx / API Gateway
    Some lightweight models (e.g., VITS, ChatTTS) can run on CPU with slower performance. However, real-time use or scaling requires a GPU.
    1. PyTorch (almost all TTS models), 2. ONNX (for optimization, if supported), 3. Docker (for containerized deployment), 4. NVIDIA Triton Inference Server (for scaling)
    For a 30-second audio clip, at least 4GB of GPU memory is required. For the 4090 GPU, it can generate audio corresponding to approximately 7 semantic tokens per second. The Real-Time Factor (RTF) is around 0.3.
    Yes, if the model supports it (e.g., Tortoise, XTTS, OpenVoice). Most require a few seconds to a minute of voice samples.
    Please make sure that the machine you are using has an NVIDIA GPU card installed and the driver is correctly installed, and the nvidia-smi command output is normal. Then, you need to install the gpu version of torch, first execute pip uninstall -y torch If your cuda is 11.x, execute pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118 If it is 12.x, execute pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
    If you use torchaudio, you need to install ffmpeg software. Download ffmpeg and add Path var on Windows, and execute on Linux apt update apt install ffmpeg -y # Sample code: torchaudio.save("output1.wav", torch.from_numpy(wavs[0]), 24000, format='wav') It is recommended to use the soundfile package pip install soundfile # Sample code: soundfile.write("output1.wav", wavs[0][0], 24000)

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