Drop-in replacement for rsxdalv/VibeVoice-Large. Qwen2-7B language model is quantized to AWQ-INT4 with Marlin GEMM kernels. The audio tokenizer + diffusion head stay FP16. Single repo, single download, standard from_pretrained — no graft step.

python

from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference
from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor
import torch

model = VibeVoiceForConditionalGenerationInference.from_pretrained(
    "ncoder-ai/VibeVoice-Large-AWQ",
    torch_dtype=torch.float16,
    device_map="cuda:0",
    attn_implementation="sdpa",
).eval()
processor = VibeVoiceProcessor.from_pretrained("ncoder-ai/VibeVoice-Large-AWQ")

That's it. The quantization_config in config.json tells transformers to swap the Qwen2 linear layers for AWQ at load time; everything else is FP16.

Why AWQ over the alternatives

VibeVoice's 7B language model dominates VRAM and inference time. Quantizing only that component keeps audio quality untouched while shrinking memory and (on most GPUs) actually speeding things up because Marlin INT4 has less memory traffic.

Metric	FP16 baseline	bnb-Q8 (FabioSarracino)	AWQ-INT4 (this)
VRAM	17.41 GB	10.84 GB	8.42 GB
RTF (i7-14700KF, 5 steps)	0.509	0.860	0.457
RTF (i5-12600K, 7 steps)	0.54	1.220	0.699

Both numbers measured on RTX 3090. The Marlin INT4 kernel is fast enough that AWQ-INT4 beats FP16 on the same hardware while bnb-Q8's per-call dispatch overhead makes it 50% slower on the slower CPU.

Audio quality A/B-tested on multi-speaker scenes (4-speaker council scene + 4- speaker contemporary scene) at 7 inference steps — no audible difference from FP16.

Calibration

Calibrated on 256 chat-style prompts (mix of long-form narration, dialog attributions, multi-speaker scripts) using auto-awq with:

4-bit, group_size=128, GEMM version, zero_point=True
Marlin kernel for inference (auto-selected by AutoAWQ on Ampere+)

The audio components (acoustic_tokenizer, semantic_tokenizer, prediction_head, acoustic_connector, semantic_connector) are excluded via modules_to_not_convert, so they load in FP16 from the same checkpoint.

Usage with the official VibeVoice library

bash

pip install transformers torch accelerate auto-awq soundfile
pip install git+https://github.com/microsoft/VibeVoice.git

The model is loaded the same way as the FP16 version:

python

from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference
from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor
import torch

MODEL = "ncoder-ai/VibeVoice-Large-AWQ"
model = VibeVoiceForConditionalGenerationInference.from_pretrained(
    MODEL, torch_dtype=torch.float16, device_map="cuda:0", attn_implementation="sdpa"
).eval()
processor = VibeVoiceProcessor.from_pretrained(MODEL)

# 7 inference steps — sweet spot for AWQ on RTX 3090 (5 steps = thinner audio)
model.set_ddpm_inference_steps(num_steps=7)

inputs = processor(
    text=["Speaker 1: Hello, this is the AWQ-quantized VibeVoice."],
    voice_samples=[["path/to/voice_sample.wav"]],
    padding=True, return_tensors="pt", return_attention_mask=True,
).to("cuda:0")

with torch.inference_mode():
    out = model.generate(
        **inputs, tokenizer=processor.tokenizer,
        cfg_scale=1.3, generation_config={"do_sample": False},
        verbose=False, refresh_negative=True,
    )

audio = out.speech_outputs[0].cpu().float().numpy().squeeze()
import soundfile as sf
sf.write("output.wav", audio, 24000)

Drop-in replacements

This model also works in:

VibeVoice-FastAPI — set VIBEVOICE_MODEL_PATH=ncoder-ai/VibeVoice-Large-AWQ and start the server. No other config needed.
VibeVoice-awq-engine — Python package that wraps this model with helpers for streaming, voice cloning, and multi-speaker scripts.

Hardware

Required: NVIDIA GPU with compute capability ≥ 7.5 (Turing or newer) for Marlin INT4 kernels
Recommended: 12 GB+ VRAM
Tested: RTX 3090 (24 GB), RTX 4070 Ti (12 GB)

License

MIT — same as the upstream rsxdalv/VibeVoice-Large. Not affiliated with Microsoft Research.