How to Setup MOSS-TTS Quantized GGUF

Running this model locally is fastest when deployed through Docker.

Make sure to follow the instructions below.

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

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

📦 Hash-sum → c461f32c1b9551dad2a58e19d633d74d | 📌 Updated on 2026-06-25 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB or higher for smooth 32k context lengths Disk Space:70 GB free space for full FP16 weights storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

MOSS-TTS is a next‑generation text‑to‑speech model that employs a transformer‑based architecture for ultra‑realistic voice generation. It supports multiple languages and dialects, delivering natural prosody and emotion through its advanced phoneme tokenizer and context‑aware encoder. The model achieves *real‑time* synthesis on consumer hardware, thanks to optimized inference kernels and a compact parameter set. A built‑in speaker embedding system allows users to personalize voice characteristics, while a *high‑fidelity* loss function ensures minimal artifacts. The following table summarizes key technical specifications for quick reference.

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