Model reference · open weights

acestep-xl

Audio ACE-Step Music / audio 2 builds Open weights 4k dl/mo

acestep-xl is an open-weight audio or speech model from ACE-Step. acestep-v15-xl-turbo-diffusers (BF16) weighs 11.1 GB; the smallest configuration that runs it is RTX 4060 Ti 16 GB.

  • acestep-xl is a text-to-audio model developed by ACE-Step that generates high-quality audio in 8 inference steps.
  • It features a 4B parameter DiT architecture and is compatible with 0.6B, 1.7B, and 4B language models.
  • The model is released under the MIT license and requires a minimum of 12 GB of VRAM when using CPU offload and INT8 quantization.

Summary of the ACE-Step/acestep-v15-xl-turbo model card, 2026-10-01. The estimate below is for another build of the family.

What it is

Released byACE-Step
Released2026-04-02
Parameters5.0B
VRAM11.1 GB for the weights

What it runs on

Memory and cards for acestep-v15-xl-turbo-diffusers (BF16)

11.1 GBweights, file size
1.6 GBruntime overhead
CardOne streamMemory
RTX 3060 12 GBdoes not fit11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
acestep-v15-xl-turbo ↗ 5.0BFP32 10.0 GBRTX 3060 12 GB
acestep-v15-xl-turbo-diffusers (above) ↗ 4.2BBF16 11.1 GBRTX 4060 Ti 16 GB

From the model card

What ACE-Step says about acestep-xl

Read the model card

Model Details

This is the XL (4B) Turbo variant of ACE-Step 1.5 — a distillation-accelerated model that generates high-quality audio in just 8 steps. Combines the speed of turbo with the quality of the 4B architecture.

XL Architecture

ParameterValue
DiT Decoder hidden_size2560
DiT Decoder layers32
DiT Decoder attention heads32
Encoder hidden_size2048
Encoder layers8
Total params~4B
Weights size (bf16)~18.8 GB
Inference steps8 (no CFG, distilled)

GPU Requirements

VRAMSupport
≥12 GBWith CPU offload + INT8 quantization
≥16 GBWith CPU offload
≥20 GBWithout offload (recommended)
≥24 GBFull quality (XL + 4B LM)

All LM models (0.6B / 1.7B / 4B) are fully compatible with XL.

Key Features

  • 💰 Commercial-Ready: Trained on legally compliant datasets. Generated music can be used for commercial purposes.
  • 📚 Safe Training Data: Licensed music, royalty-free/public domain, and synthetic (MIDI-to-Audio) data.
  • ⚡ Fast: 8-step inference — the fastest XL variant.
  • 🔮 Higher Quality: 4B parameters provide richer audio quality than 2B turbo.

Quick Start

# Install ACE-Step
git clone https://github.com/ace-step/ACE-Step-1.5.git
cd ACE-Step-1.5
pip install -e .

# Download this model
huggingface-cli download ACE-Step/acestep-v15-xl-turbo --local-dir ./checkpoints/acestep-v15-xl-turbo

# Run with Gradio UI
python acestep --config-path acestep-v15-xl-turbo

Model Zoo

XL (4B) DiT Models

DiT ModelCFGStepsQualityDiversityTasksHugging FaceModelScope
acestep-v15-xl-base✅50HighHighAll (extract, lego, complete)LinkLink
acestep-v15-xl-sft✅50Very HighMediumStandardLinkLink
acestep-v15-xl-turbo❌8Very HighMediumStandardThis repoLink

LM Models (all compatible with XL)

LM ModelParamsAudio UnderstandingCompositionHugging FaceModelScope
acestep-5Hz-lm-0.6B0.6BMediumMediumLinkLink
acestep-5Hz-lm-1.7B1.7BMediumMediumIncluded in mainIncluded in main
acestep-5Hz-lm-4B4BStrongStrongLinkLink

Acknowledgements

This project is co-led by ACE Studio and StepFun.

Citation

@misc{gong2026acestep,
    title={ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation},
    author={Junmin Gong, Yulin Song, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo},
    howpublished={\url{https://github.com/ace-step/ACE-Step-1.5}},
    year={2026},
    note={GitHub repository}
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. How to run this model is on its model card.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms