DFloat11: Lossless LLM Compression for Efficient GPU Inference

DFloat11 is a lossless compression framework that reduces the size of Large Language Models (LLMs) 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.

📰 News

• [05/06/2025] DFloat11 now supports FLUX.1-dev
  • 🖼️ Generate stunning text-to-image results on GPUs with less than 24GB VRAM --- no quality lost!
  • 📂 Get started here: examples/flux.1.
• [05/05/2025] The dfloat11 pip package has been upgraded to v0.2.0! Run pip install -U dfloat11[cuda12] to upgrade to the latest version. We have made the following important changes:
  • We added support for Qwen 3, Gemma 3, and Phi 4!
  • The GPU decompression kernel is now 20-40% faster! We achieved it by improving thread occupancy and implementing tons of optimizations.
  • The DFloat11 models are now stored in safetensors format for better safety and loading performance.
  • When using a DFloat11 model, only the compressed model is downloaded, not the original model.

📦 Installation

Requires a CUDA-compatible GPU and PyTorch installed.

pip install -U dfloat11[cuda12]
# or if you have CUDA version 11:
# pip install -U dfloat11[cuda11]

🔍 How It Works

DFloat11 compresses model weights using Huffman coding of BFloat16 exponent bits, combined with hardware-aware algorithmic designs that enable efficient on-the-fly decompression directly on the GPU. During inference, the weights remain compressed in GPU memory and are decompressed just before matrix multiplications, then immediately discarded after use to minimize memory footprint.

Key benefits:

• No CPU decompression or host-device data transfer: all operations are handled entirely on the GPU.
• Decompression overhead is constant per forward pass and independent of batch size, making DFloat11 increasingly efficient at larger batch sizes.
• DFloat11 is much faster than CPU-offloading approaches, enabling practical deployment in memory-constrained environments.
• At batch size = 1, inference is approximately 2× slower than the original BF16 model, but the performance gap narrows significantly with larger batches.
• The compression is fully lossless, guaranteeing that the model’s outputs are bit-for-bit identical to those of the original model.

🚀 Quick Start

1. Install the dfloat11 pip package. See Installation.
2. Run the following code in Python, which automatically downloads the DFloat11 Qwen3-8B model and generates a response.

import torch
from dfloat11 import DFloat11Model
from transformers import AutoTokenizer

model_id = "DFloat11/Qwen3-8B-DF11"

model = DFloat11Model.from_pretrained(model_id, device_map="auto")

tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

prompt = "Question: What is a binary tree and its applications? Answer:"
inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
    )

print(tokenizer.batch_decode(output, skip_special_tokens=True))

3. Replace the model_id in the script above with any pre-compressed model in the Model Hub.

🏎️ Benchmarking Performance

To test the speed and memory consumption a DFloat11 LLM during inference:

CUDA_VISIBLE_DEVICES=0 python inference.py \
  --model_name_or_path DFloat11/Qwen3-8B-DF11 \
  --prompt "Question: What is a binary tree and its applications? Answer:" \
  --num_tokens 512 \
  --batch_size 1

💡 Tip: If you specify multiple CUDA devices (e.g., CUDA_VISIBLE_DEVICES=0,1), the model will be automatically distributed across them using 🤗 Accelerate's device_map="auto".

Arguments

• --model_name_or_path: HuggingFace name or local path of the DFloat11 model (e.g., DFloat11/Qwen3-8B-DF11). See the Model Hub section for a list of available DFloat11 models.
• --bf16: (Optional) Turn on this flag when passing a BFloat16 model to --model_name_or_path
• --prompt: Input prompt string for text generation
• --num_tokens: Number of new tokens to generate per sample
• --batch_size: Number of prompts to process in parallel
• --seed: (Optional) Random seed for reproducible results

Output

The script prints:

• Generated responses
• Total decoding latency
• Tokens per second (throughput)
• GPU memory usage (allocated and peak)

📚 Model Hub

Model	DFloat11 Link
FLUX.1-dev	DFloat11/FLUX.1-dev-DF11
Qwen 3 32B	DFloat11/Qwen3-32B-DF11
Qwen 3 14B	DFloat11/Qwen3-14B-DF11
Qwen 3 8B	DFloat11/Qwen3-8B-DF11
Qwen 3 4B	DFloat11/Qwen3-4B-DF11
Phi 4 Reasoning Plus	DFloat11/Phi-4-reasoning-plus-DF11
Gemma 3 27B Instruct	DFloat11/gemma-3-27b-it-DF11
Gemma 3 12B Instruct	DFloat11/gemma-3-12b-it-DF11
Gemma 3 4B Instruct	DFloat11/gemma-3-4b-it-DF11
Llama 3.1 8B Instruct	DFloat11/Llama-3.1-8B-Instruct-DF11
DeepSeek R1 Distill Qwen 32B	DFloat11/DeepSeek-R1-Distill-Qwen-32B-DF11
DeepSeek R1 Distill Qwen 14B	DFloat11/DeepSeek-R1-Distill-Qwen-14B-DF11
DeepSeek R1 Distill Qwen 7B	DFloat11/DeepSeek-R1-Distill-Qwen-7B-DF11
DeepSeek R1 Distill Llama 8B	DFloat11/DeepSeek-R1-Distill-Llama-8B-DF11
Discover more models on our HF page!	...

How to Use a DFloat11 Model

1. Download a model using the HuggingFace command line tool:

huggingface-cli download \
  DFloat11/Llama-3.1-8B-Instruct-DF11 \     # DFloat11 model name
  --local-dir ./Llama-3.1-8B-Instruct-DF11  # local path to download the DFloat11 model

2. Run the following in Python to load the model and tokenizer:

from dfloat11 import DFloat11Model
from transformers import AutoTokenizer

model_path = "./Llama-3.1-8B-Instruct-DF11"
model = DFloat11Model.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)

🔗 Links

👉 Explore pre-compressed DFloat11 models ready to use on HuggingFace: https://huggingface.co/DFloat11

📂 Official Code Repository: https://github.com/LeanModels/DFloat11

🧠 Contributions

This work is brought to you by the team at Rice University and xMAD.ai.

The GPU kernel was designed and implemented by Tianyi Zhang.

📚 Citation

If you found our work useful or interesting, please consider citing our paper:

@article{zhang2025dfloat11,
  title={70\% Size, 100\% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float},
  author={Zhang, Tianyi and Sui, Yang and Zhong, Shaochen and Chaudhary, Vipin and Hu, Xia and Shrivastava, Anshumali},
  journal={arXiv preprint arXiv:2504.11651},
  year={2025}
}