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VoxCPM2 on AMD/Nvidia GPU Easy Build

VoxCPM2 on AMD/Nvidia GPU Easy Build

The fastest way to get this model running locally is via Docker.

Just follow the guidelines provided below.

The setup auto-streams the model assets (expect a multi-GB download).

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

🗂 Hash: 8b57b5901556ed4254e6b4952bcb9800Last Updated: 2026-06-25



  • Processor: 6-core 3.5 GHz minimum required
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Storage: extra room for future model updates and datasets
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

VoxCPM2 is a next‑generation speech synthesis model designed to generate highly natural‑sounding audio across dozens of languages. It leverages a conditional parameterization approach that reduces memory footprint by up to 60 % while preserving voice fidelity. The architecture integrates a hierarchical encoder and a diffusion‑based decoder, enabling real‑time inference with latency under 150 ms on standard hardware. A built‑in speaker adaptation module allows users to personalize voice models with just a few seconds of audio, eliminating the need for extensive retraining. These capabilities are showcased in a comparative benchmark where VoxCPM2 outperforms prior models on MOS scores, word error rates, and multilingual consistency, as detailed in the table below.

Metric VoxCPM2 Prior Model
MOS Score 4.62 4.31
Word Error Rate (%) 5.8 7.4
Multilingual Consistency 92% 84%
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