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Lightweight AI Inference Engine — All-in-one Solution for Text, Vision, Speech, and OCR

aha is a high-performance, cross-platform AI inference engine built with Rust and the Candle framework. It brings state-of-the-art AI models to your local machine—no API keys, no cloud dependencies, just pure, fast AI running directly on your hardware.

• Added Qwen3-ASR speech recognition model

• Added CLI list subcommand to show supported models
• Added CLI subcommand structure support (cli, serv, download, run)
• Fixed Qwen3VL thinking startswith bug
• Fixed aha run multiple inputs bug

• Added Qwen3 text model support
• Added Fun-ASR-Nano-2512 speech recognition model
• Fixed ModelScope Fun-ASR-Nano model load error
• Updated audio resampling with rubato

• Added GLM-ASR-Nano-2512 speech recognition model
• Merged Metal (GPU) support for Apple Silicon
• Added dynamic home directory and model download script

View full changelog →

git clone https://github.com/jhqxxx/aha.git
cd aha
cargo build --release

Optional Features:

# CUDA (NVIDIA GPU acceleration)
cargo build --release --features cuda

# Metal (Apple GPU acceleration for macOS)
cargo build --release --features metal

# Flash Attention (faster inference)
cargo build --release --features cuda,flash-attn

# FFmpeg (multimedia processing)
cargo build --release --features ffmpeg

# List all supported models
aha list

# Download model only
aha download -m qwen3asr-0.6b

# Download model and start service
aha -m qwen3asr-0.6b

# Run inference directly (without starting service)
aha run -m qwen3asr-0.6b -i "audio.wav"

# Start service only (model already downloaded)
aha serv -m qwen3asr-0.6b -p 10100

aha serv -m qwen3-0.6b -p 10100

Then use the unified (OpenAI-compatible) API:

curl http://localhost:10100/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-0.6b",
    "messages": [{"role": "user", "content": "Hello!"}],
    "stream": false
  }
'

• 🚀 High-Performance Inference - Powered by Candle framework for efficient tensor computation and model inference
• 🔧 Unified Interface — One tool for text, vision, speech, and OCR
• 📦 Local-First — All processing runs locally, no data leaves your machine
• 🎯 Cross-Platform — Works on Linux, macOS, and Windows
• ⚡ GPU Accelerated — Optional CUDA support for faster inference
• 🛡️ Memory Safe — Built with Rust for reliability
• 🧠 Attention Optimization - Optional Flash Attention support for optimized long sequence processing

cargo add aha

# VoxCPM example
use aha::models::voxcpm::generate::VoxCPMGenerate;
use aha::utils::audio_utils::save_wav;
use anyhow::Result;

fn main() -> Result<()> {
    let model_path = "xxx/openbmb/VoxCPM-0.5B/";

    let mut voxcpm_generate = VoxCPMGenerate::init(model_path, None, None)?;

    let generate = voxcpm_generate.generate(
        "The sun is shining bright, flowers smile at me, birds say early early early".to_string(),
        None,
        None,
        2,
        100,
        10,
        2.0,
        false,
        6.0,
    )?;

    let _ = save_wav(&generate, "voxcpm.wav")?;
    Ok(())
}

• Create new model file in src/models/
• Export in src/models/mod.rs
• Add support for CLI model inference in src/exec/
• Add tests and examples in tests/

• High-performance inference via Candle framework
• Multi-modal model support (vision, language, speech)
• Clean, easy-to-use API design
• Minimal dependencies, compact binaries
• Flash Attention support for long sequences
• FFmpeg support for multimedia processing

Apache-2.0 — See LICENSE for details.

• Candle - Excellent Rust ML framework
• All model authors and contributors

_{Built with ❤️ by the aha team}

_{We're continuously expanding our model support. Contributions are welcome!}

_{If this project helps you, please consider giving us a ⭐ Star!}