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

Go through the configuration rules shown below.

The loader auto-caches the model archive (several GBs included).

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

🔒 Hash checksum: 4172c515c5164d8cd2d79d4336a1b6dd • 📆 Last updated: 2026-06-28 Verify Processor: high single-core performance needed for token latency RAM: minimum 16 GB for stable 8B model loading Storage:100 GB free space for HuggingFace cache folder Graphics: 12 GB VRAM minimum required for basic quantization

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.

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