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---
base_model: google/gemma-3-27b-it
base_model_relation: quantized
tags:
- dfloat11
- df11
- lossless compression
- 70% size, 100% accuracy
---
## DFloat11 Compressed Model: `google/gemma-3-27b-it`
This is a **losslessly compressed** version of [`google/gemma-3-27b-it`](https://huggingface.co/google/gemma-3-27b-it) using our custom **DFloat11** format. The outputs of this compressed model are **bit-for-bit identical** to the original BFloat16 model, while reducing GPU memory consumption by approximately **30%**.
### 🔍 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.
### 🔧 How to Use
1. Install the DFloat11 pip package *(installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed)*:
```bash
pip install dfloat11[cuda12]
# or if you have CUDA version 11:
# pip install dfloat11[cuda11]
```
2. To use the DFloat11 model, run the following example code in Python:
```python
import torch
from dfloat11 import DFloat11Model
from transformers import AutoTokenizer
model_id = "DFloat11/gemma-3-27b-it-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))
```
### 📄 Learn More
* **Paper**: [70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float](https://arxiv.org/abs/2504.11651)
* **GitHub**: [https://github.com/LeanModels/DFloat11](https://github.com/LeanModels/DFloat11)
* **HuggingFace**: [https://huggingface.co/DFloat11](https://huggingface.co/DFloat11) |