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The Best GPU Plans for XTTS-v2 Hosting

Choose the appropriate GPU according to the XTTS-v2 model size(2GB).

Basic GPU Dedicated Server - GTX 1660

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  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1660
  • Dual 8-Core Xeon E5-2660
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Turing
  • CUDA Cores: 1408
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 5.0 TFLOPS

Professional GPU Dedicated Server - RTX 2060

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  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 8-Core E5-2660
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 1920
  • Tensor Cores: 240
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 6.5 TFLOPS

Basic GPU Dedicated Server - T1000

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  • 64GB RAM
  • GPU: Nvidia Quadro T1000
  • Eight-Core Xeon E5-2690
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Turing
  • CUDA Cores: 896
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 2.5 TFLOPS

Basic GPU Dedicated Server - RTX 4060

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  • 64GB RAM
  • GPU: Nvidia GeForce RTX 4060
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 3072
  • Tensor Cores: 96
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 15.11 TFLOPS

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

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

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.

Advanced GPU Dedicated Server - V100

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  • 128GB RAM
  • GPU: Nvidia V100
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

The Best GPUs for XTTS Models from Hugging Face

When deploying XTTS models like XTTS-v2 or XTTS-v1 from Hugging Face, GPU selection significantly impacts performance, especially for voice cloning and real-time inference. Entry-level GPUs like the GTX 1650 and GTX 1660 can run the models with slower inference speeds and are suitable for testing or offline batch generation. Mid-tier cards like RTX 3060 Ti and NVIDIA A4000 strike a great balance between cost and capability.

Model Name
Size (4-bit Quantization)
Recommended GPUs
coqui/XTTS-v2 2 GB GTX1650 < GTX1660 < RTX2060 < RTX4060 < GTX3060ti = A4000 < V100
coqui/XTTS-v1 3 GB GTX1650 < GTX1660 < RTX2060 < RTX4060 < GTX3060ti = A4000 < V100

Features of XTTS-v2 Service Hosting

Multilingual Support

Generate speech in multiple languages with consistent voice across languages—ideal for global applications.

Cross-Lingual Voice Cloning

Clone a speaker's voice using just a few seconds of audio, then synthesize speech in different languages with the same vocal identity.

Lightweight Model (~2GB)

Optimized for fast startup and deployment on mid-tier GPU or even CPU servers, making it highly cost-efficient.

Self-Hosted Privacy

Run the model on your own infrastructure to maintain full control of your data and voice models—no third-party dependencies.

Real-Time Inference Ready

Supports low-latency generation for real-time applications like chatbots, voice assistants, and streaming TTS services.

Open Source Flexibility

No licensing fees or restrictions—customize and scale the model as needed for research or commercial use.

Frequently asked questions

XTTS hosting is to deploying the XTTS-v2 (cross-lingual text-to-speech) models from Coqui.ai on a server, usually with a GPU, to generate realistic speech audio from text input.
It is technically possible but not recommended. CPU inference is extremely slow and inefficient. A GPU-based VPS or dedicated server is required for production or real-time applications.
Yes. XTTS Service allows few-shot speaker cloning using just a short audio sample (about 3–5 seconds), and can retain emotional tone and multilingual capability.
Yes. XTTS Service is commonly integrated via FastAPI, Flask, or Gradio UIs. You can wrap the inference script into an API for easy consumption by web or mobile clients.
XTTS is released under a license that allows commercial use, but it’s important to check the specific license terms on Hugging Face or the Coqui site before deployment.
XTTS-v2 (about 2GB) can run on GPUs with ≥4GB VRAM, but for better performance and real-time inference, a 6GB+ VRAM GPU such as GTX 1660, RTX 2060, or higher is recommended.
Multilingual TTS generation
AI voice bots or assistants
Audiobook and content narration
Voice cloning for custom speakers
Edge-based voice services with privacy control
No. Once the model and speaker embeddings are loaded, inference can run fully offline on your server.
Absolutely. XTTS is compatible with Docker-based environments. This ensures consistent setup and simplifies deployment across servers.
XTTS offers:
Cross-lingual synthesis
Real-time inference on modest GPUs
Lightweight model size (~2GB)
Voice cloning with better latency than Bark or Tortoise

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