AutoRound is an advanced quantization toolkit designed for Large Language Models (LLMs) and Vision-Language Models (VLMs). It achieves high accuracy at ultra-low bit widths (2–4 bits) with minimal tuning by leveraging sign-gradient descent and providing broad hardware compatibility. See our papers SignRoundV1 and SignRoundV2 for more details. For usage instructions, please refer to the User Guide.

• [2026/03] Block-wise FP8 quantization is available via --scheme FP8_BLOCK --iters 0 --disable_opt_rtn.

• [2026/03] MTP layer quantization has been supported in this PR

• [2025/12] The SignRoundV2 paper is available. Turn on enable_alg_ext and use the AutoScheme API for mixed-precision quantization to reproduce the results: Paper, Notes for evaluating LLaMA models.

• [2025/11] AutoRound has landed in LLM-Compressor: Usage, vLLM blog, RedHat blog, X post, Intel blog, Linkedin, 微信, 知乎.

• [2025/11] An enhanced GGUF quantization algorithm is available via --enable_alg_ext: Accuracy.

• [2025/10] AutoRound has been integrated into SGLang: Usage, LMSYS Blog, X post, Intel blog, Linkedin.

• [2025/10] A mixed precision algorithm is available to generate schemes in minutes: Usage, Accuracy.

• [2025/09] MXFP4 and NVFP4 dtypes is available: Accuracy.

• [2025/08] An improved INT2 algorithm is available via --enable_alg_ext: Accuracy

• [2025/07] GGUF format is supported: Usage.

• [2025/05] AutoRound has been integrated into vLLM: Usage, Medium blog, 小红书.

• [2025/05] AutoRound has been integrated into Transformers: Blog.

• [2025/03] The INT2-mixed DeepSeek-R1 model (~200GB) retains 97.9% accuracy: Model.

✅ Superior Accuracy Delivers strong performance even at 2–3 bits example models, with leading results at 4 bits benchmark.

✅ Ecosystem Integration Seamlessly works with Transformers, vLLM, SGLang and more.

✅ Multiple Formats Export Support AutoRound, AutoAWQ, AutoGPTQ, and GGUF for maximum compatibility. Details are shown in export formats

✅ Fast Mixed Bits/Dtypes Scheme Generation Automatically configure in minutes, with about 1.1X-1.5X the model’s BF16 RAM size as overhead. Accuracy results and user guide.

✅ Optimized Round-to-Nearest Mode Use --iters 0 for fast quantization with some accuracy drop for 4 bits. Details are shown in opt_rtn mode

✅ Affordable Quantization Cost Quantize 7B models in about 10 minutes on a single GPU. Details are shown in quantization costs

✅ 10+ VLMs Support Out-of-the-box quantization for 10+ vision-language models example models, support matrix

✅ Multiple Recipes Choose from auto-round-best, auto-round, and auto-round-light to suit your needs. Details are shown in quantization recipes

✅ Advanced Utilities Includes multiple gpus quantization, multiple calibration datasets and support for 10+ runtime backends.

✅ Beyond weight only quantization. We are actively expanding support for additional datatypes such as MXFP, NVFP, W8A8, and more.

# CPU(Xeon)/GPU(CUDA)
pip install auto-round

# CPU(Xeon)/GPU(CUDA) nightly
pip install auto-round-nightly

# HPU(Gaudi)
# install inside the hpu docker container, e.g. vault.habana.ai/gaudi-docker/1.23.0/ubuntu24.04/habanalabs/pytorch-installer-2.9.0:latest  
pip install auto-round-hpu

# XPU(Intel GPU)
pip install torch --index-url https://download.pytorch.org/whl/xpu
pip install auto-round

# CPU(Xeon)/GPU(CUDA)
pip install .

# HPU(Gaudi)
python setup.py install hpu

# XPU(Intel GPU)
pip install torch --index-url https://download.pytorch.org/whl/xpu
pip install .

If you encounter issues during quantization, try using pure RTN mode with iters=0, disable_opt_rtn=True. Additionally, using group_size=32 or mixed bits is recommended for better results.

The full list of supported arguments is provided by calling auto-round -h on the terminal.

ModelScope is supported for model downloads, simply set AR_USE_MODELSCOPE=1.

auto-round \
    --model Qwen/Qwen3-0.6B \
    --scheme "W4A16" \
    --format "auto_round" \
    --output_dir ./tmp_autoround

We offer another two recipes, auto-round-best and auto-round-light, designed for optimal accuracy and improved speed, respectively. Details are as follows.

# Best accuracy, 3X slower, low_gpu_mem_usage could save ~20G but ~30% slower
auto-round-best \
  --model Qwen/Qwen3-0.6B \
  --scheme "W4A16" \
  --low_gpu_mem_usage 

# 2-3X speedup, slight accuracy drop at W4 and larger accuracy drop at W2
auto-round-light \
  --model Qwen/Qwen3-0.6B \
  --scheme "W4A16" 

In conclusion, we recommend using auto-round for W4A16 and auto-round-best with enable_alg_ext for W2A16. However, you may adjust the configuration to suit your specific requirements and available resources.

from auto_round import AutoRound

# Load a model (supports FP8/BF16/FP16/FP32)
model_name_or_path = "Qwen/Qwen3-0.6B"

# Available schemes: "W2A16", "W3A16", "W4A16", "W8A16", "NVFP4", "MXFP4" (no real kernels), "GGUF:Q4_K_M", etc.
ar = AutoRound(model_name_or_path, scheme="W4A16")

# Highest accuracy (4–5× slower).
# `low_gpu_mem_usage=True` saves ~20GB VRAM but runs ~30% slower.
# ar = AutoRound(model_name_or_path, nsamples=512, iters=1000, low_gpu_mem_usage=True)

# Faster quantization (2–3× speedup) with slight accuracy drop at W4G128.
# ar = AutoRound(model_name_or_path, nsamples=128, iters=50, lr=5e-3)

# Supported formats: "auto_round" (default), "auto_gptq", "auto_awq", "llm_compressor", "gguf:q4_k_m", etc.
ar.quantize_and_save(output_dir="./qmodel", format="auto_round")

AutoScheme provides an automatic algorithm to generate adaptive mixed bits/data-type quantization recipes. Please refer to the user guide for more details on AutoScheme.

from auto_round import AutoRound, AutoScheme

model_name = "Qwen/Qwen3-8B"
avg_bits = 3.0
scheme = AutoScheme(avg_bits=avg_bits, options=("GGUF:Q2_K_S", "GGUF:Q4_K_S"), ignore_scale_zp_bits=True)
layer_config = {"lm_head": "GGUF:Q6_K"}

# Change iters to 200 for non-GGUF schemes
ar = AutoRound(model=model_name, scheme=scheme, layer_config=layer_config, iters=0)
ar.quantize_and_save()

from auto_round import AutoRound

# Load the model
model_name_or_path = "Qwen/Qwen2.5-VL-7B-Instruct"
# Quantize the model
ar = AutoRound(model_name_or_path, scheme="W4A16")
output_dir = "./qmodel"
ar.quantize_and_save(output_dir)

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
]
sampling_params = SamplingParams(temperature=0.6, top_p=0.95)
model_name = "Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound"
llm = LLM(model=model_name)

outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

Please note that support for the MoE models and visual language models is currently limited.

import sglang as sgl

llm = sgl.Engine(model_path="Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound")
prompts = [
    "Hello, my name is",
]
sampling_params = {"temperature": 0.6, "top_p": 0.95}

outputs = llm.generate(prompts, sampling_params)
for prompt, output in zip(prompts, outputs):
    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

AutoRound supports 10+ backends and automatically selects the best available backend based on the installed libraries and prompts the user to install additional libraries when a better backend is found.

Please avoid manually moving the quantized model to a different device (e.g., model.to('cpu')) during inference, as this may cause unexpected exceptions.

The support for Gaudi device is limited.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound"
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
text = "There is a girl who likes adventure,"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50)[0]))

SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs (2025.12 paper)

Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLM (2023.09 paper)

TEQ: Trainable Equivalent Transformation for Quantization of LLMs (2023.10 paper)

Effective Post-Training Quantization for Large Language Models (2023.04 blog)

Check out Full Publication List.

Special thanks to open-source low precision libraries such as AutoGPTQ, AutoAWQ, GPTQModel, Triton, Marlin, and ExLLaMAV2 for providing low-precision CUDA kernels, which are leveraged in AutoRound.

If you find AutoRound helpful, please ⭐ star the repo and share it with your community!