This is a weight-only FP8 quantization of LongCat-AudioDiT-3.5B — a state-of-the-art diffusion-based zero-shot TTS model by Meituan that operates directly in the waveform latent space. The quantization cuts the on-disk size roughly in half and reduces VRAM usage from ~14 GB to ~4 GB, with no perceptible quality loss in practice.
Original (3.5B FP32)
This (3.5B FP8)
Weight dtype
float32
float8_e4m3fn
Activation dtype
bfloat16
bfloat16
Scale
—
per-tensor float32
File size
~14 GB
~4 GB
VRAM (inference)
~20 GB
~8 GB
Extra dependencies
none
none
Quantization Details
What is quantized: All linear weight matrices in the DiT transformer backbone. Non-linear weights (VAE, embeddings, layer norms, text encoder) remain in bfloat16.
Method: Per-tensor symmetric FP8
Each weight matrix is quantized using a per-tensor scale factor stored in fp8_scales.json:
No external quantization library required. Dequantization is handled automatically by the ComfyUI-LongCat-AudioDIT-TTS node pack at load time. The model is fully compatible with the original audiodit inference code once dequantized.
File layout:
model.safetensors — FP8 weight tensors
fp8_scales.json — per-tensor float32 scale factors for dequantization
Hardware Requirements
GPU: NVIDIA GPU with CUDA support
VRAM: ~8 GB
Native FP8 tensor cores: Ada Lovelace or Blackwell (RTX 4090, RTX 5090, H100, etc.) — recommended for full speed
Older GPUs (Ampere and below): Will load and run correctly. Dequantization to bfloat16 happens on all hardware, so you still get the reduced VRAM footprint even without native FP8 cores.
Usage — ComfyUI (Recommended)
The easiest way to use this model is with ComfyUI-LongCat-AudioDIT-TTS, which has native support for this FP8 model with zero extra setup.
Installation
Install the ComfyUI node via ComfyUI Manager (search LongCat-AudioDiT) or manually:
dtype: auto or bf16 — FP8 weights are dequantized to BF16 at load time
guidance_method: cfg for TTS, apg for voice cloning
steps: 16 (balanced), 32 (higher quality)
keep_model_loaded: True for repeated use
About LongCat-AudioDiT
LongCat-AudioDiT is a non-autoregressive diffusion-based TTS model from Meituan that achieves state-of-the-art zero-shot voice cloning performance on the Seed benchmark. Unlike previous methods relying on mel-spectrograms, it operates directly in the waveform latent space using only a Wav-VAE and a DiT backbone.
The 3.5B variant achieves 0.818 SIM on Seed-ZH and 0.797 SIM on Seed-Hard, surpassing both open-source and closed-source competitors.
