⚡️ DFloat11: Lossless LLM Compression for Efficient GPU Inference
DFloat11 is a lossless compression framework that reduces the size of LLMs and Diffusion Models by approximately 30% while preserving bit-for-bit identical outputs to the original model. It enables efficient GPU inference on resource-constrained hardware without sacrificing accuracy.
🚀 Key Features
- Lossless Compression: Achieves ~30% model size reduction with outputs identical to the original BFloat16 models.
- GPU-Efficient: All decompression is handled on-GPU, eliminating CPU overhead and host-device data transfers.
- Scalable Performance: Decompression overhead remains constant per forward pass and is independent of batch size.
- Broad Model Support: Compatible with various models, including Qwen3, Gemma3, Llama3, Phi4, Wan2.1, FLUX.1, and BAGEL.
🛠 Installation
Ensure you have a CUDA-compatible GPU and PyTorch installed.
pip install -U dfloat11[cuda12]
pip install -U dfloat11[cuda11]
🧪 Quick Start
For example usage, refer to the examples directory in the GitHub repository.
📄 Learn More