Coqui TTS Hosting | Open-Source Text-to-Speech on GPU – B2BHostingClub

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Choose The Best GPU Server for Coqui TTS Hosting

Our platform runs Coqui TTS on optimized inference servers (GPU-accelerated) to deliver sub-second response times and high throughput. Whether you demand single-user responsiveness (e.g., voice assistant) or bulk generation (e.g., audiobook production), our architecture scales to meet your needs.

Advanced GPU Dedicated Server - RTX 3060 Ti

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  • 128GB RAM
  • GPU: GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • 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 5060

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  • 64GB RAM
  • GPU: Nvidia GeForce RTX 5060
  • 24-Core Platinum 8160
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Blackwell 2.0
  • CUDA Cores: 4608
  • Tensor Cores: 144
  • GPU Memory: 8GB GDDR7
  • FP32 Performance: 23.22 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–7 days after payment.

Professional GPU VPS - A4000

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  • 32GB RAM
  • Dedicated GPU: Quadro RTX A4000
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth
  • OS: Linux / Windows 10/11
  • Once per 2 Weeks Backup
  • Single GPU Specifications:
  • CUDA Cores: 6,144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS

Advanced GPU Dedicated Server - A5000

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  • 128GB RAM
  • GPU: Nvidia Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS

Enterprise GPU Dedicated Server - RTX 4090

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  • 256GB RAM
  • GPU: GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS

Enterprise GPU Dedicated Server - RTX 5090

/mo

  • 256GB RAM
  • GPU: GeForce RTX 5090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Blackwell 2.0
  • CUDA Cores: 21,760
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–10 days after payment.

Advanced GPU VPS - RTX 5090

/mo

  • 96GB RAM
  • Dedicated GPU: GeForce RTX 5090
  • 32 CPU Cores
  • 400GB SSD
  • 500Mbps Unmetered Bandwidth
  • OS: Linux / Windows 10/11
  • Once per 2 Weeks Backup
  • Single GPU Specifications:
  • CUDA Cores: 21,760
  • Tensor Cores: 680
  • GPU Memory: 32GB GDDR7
  • FP32 Performance: 109.7 TFLOPS

Coqui TTS vs Chatterbox TTS

Here’s a comparison between Coqui TTS and Chatterbox TTS — two open-source text-to-speech (TTS) toolkits/models. I’ll cover their key features, strengths, weaknesses and suitable use-cases so you can decide which might fit your needs best.

Feature
Coqui TTS
Chatterbox TTS
Origin & licensing Coqui TTS is a toolkit originally developed (forked) from the Mozilla/Coqui TTS project. It supports a wide range of models and languages. The project’s company (Coqui AI) announced shutdown of hosted services in late 2023/early 2024, though the open-source code remains. Chatterbox TTS is developed by Resemble AI, released as an open-source model under MIT license.
Model scope / language support Supports many models, including the “XTTS-v2” model: supports 17 languages. Also claims “+1100 languages” via certain frameworks. Supports 23+ languages in the Chatterbox Multilingual model.
Voice cloning & zero-shot capabilities XTTS-v2 supports voice cloning with just a short reference audio clip (6 seconds) and cross-language voice cloning. Zero-shot voice cloning is a prominent feature: clone voices from a few seconds of reference audio; includes “emotion/exaggeration control.”
Emotion / style control Coqui supports style and voice cloning, but less emphasis (in marketing) on exaggerated emotion controls. Chatterbox emphasises expressive/emotional control (“exaggeration/intensity control”) as a key differentiator.
Intended audience & usability Strong toolkit orientation: many models, training/fine-tuning, researcher/developer focus. Eg: blog says “for software engineers and data scientists.” More turnkey/model-oriented: the model itself is emphasised, with developers/creators in mind (games, video, agents) and easy reference audio support.
Performance / latency claims Documentation indicates streaming inference with < 200 ms latency under “XTTS” model. Claims “ultra-low latency of sub 200ms” for production use in interactive media.
Model maturity / ecosystem Larger and more mature ecosystem of tools, many models, fine-tuning support, dataset utilities. Very recent release (as of 2024/2025), high quality, but fewer years of ecosystem maturity compared to Coqui’s history.
Community feedback & limitations Some community commentary: e.g., one Reddit user: “Cloned voice does not feel like clone (although it did have some features of the source voice).” Also note the company shutdown means less commercial backing, maybe less support/maintenance. Early reviews highlight excellent cloning and expressiveness; but some users mention install / dependency issues.
Licensing & commercial use The code is open source; however you’ll want to confirm specific model license and commercial-use restrictions. The company’s shutdown may impact future updates/hosting. MIT-licensed model (Chatterbox) means very permissive use, which is a strong plus.
Best suited for Projects where you want full control: self-hosting, fine-tuning/custom voices, many languages, training your own models. Projects where you care most about voice quality, expressiveness, voice-cloning ease, and want a “plug-in” model ready for use without heavy training.

Key Features & Capabilities of Hosted Coqui TTS

Pre-trained models

You don’t always need to train from scratch—many ready-to-use models exist.

Multi-speaker

It can generate speech in many languages and switch speakers.

Multilingual support

It supports many languages (hundreds of models across more than a thousand languages in some configurations) and multiple speakers/voices.

Voice cloning & style transfer

For example the “XTTS-v2” model supports voice cloning with a short sample (e.g., 6 seconds) and cross-language voice transfer.

Deployment flexibility

Works via Python API, command line, and even as a local server.

Simplity & Scalability

Utilities to use and test your models. Modular (but not too much) code base enabling easy implementation of new ideas.

Frequently asked questions

Coqui TTS is an open-source text-to-speech (“TTS”) toolkit for converting written text into spoken audio. It supports many languages (hundreds of models across more than a thousand languages in some configurations) and multiple speakers/voices. It can be used for voice cloning, multilingual TTS, and fine-tuning custom voices.
The core XTTS-v2 model supports 17 languages including English, Spanish, French, German, Portuguese, Russian, Arabic, Chinese, Japanese, Korean, Hungarian, Hindi and more.
Yes — voice cloning is supported with as little as 6 seconds of reference audio in the XTTS-v2 model.
For a hosting/inference scenario – here are recommended specs:
Entry hosting: One GPU with ~8 GB VRAM (e.g., NVIDIA RTX 3060Ti 8GB) — good for small-scale hosting, light concurrency.
Mid hosting: One GPU ~16-24 GB VRAM (e.g., RTX A4000 16GB / RTX 4090 / 24 GB class) — better for moderate concurrency, multiple voices, higher throughput.
High-throughput / multi-tenant hosting: Multiple GPUs or one large GPU (e.g., RTX 5090 32 GB VRAM), high memory, fast IO. For many simultaneous requests, low latency, many voices.
Yes — but you should ensure your usage of Coqui models complies with licensing terms. The XTTS-v2 model is licensed under the Coqui Public Model License.
None — we host it for you. If you choose self-hosting (on-premises or in your cloud), you’ll want a GPU-accelerated server for best performance.

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