AutoRound: Making Large Language Model Quantization Simple and Efficient

In today’s rapidly evolving AI landscape, large language models (LLMs) have become increasingly powerful but also increasingly demanding in terms of computational resources. As these models grow larger, deploying them on standard hardware or edge devices becomes challenging. This is where model quantization comes into play—a technique that reduces model size while maintaining acceptable performance. Among the various quantization tools available, AutoRound stands out as a particularly effective solution. In this comprehensive guide, we’ll explore what makes AutoRound special, how it works, and how you can leverage it to optimize your AI models.

Understanding the Need for Model Quantization

Before diving into AutoRound specifically, let’s establish why model quantization matters in the first place. Large language models typically store their parameters using 16-bit or 32-bit floating point numbers (FP16 or FP32). While this provides high precision, it comes at a significant cost in terms of memory usage and computational requirements.

For example, a 7-billion parameter model using FP16 would require approximately 14GB of memory just to store the weights. This makes deployment challenging on consumer-grade hardware, mobile devices, or in resource-constrained environments. Model quantization addresses this by representing weights with fewer bits—typically 8, 4, or even 2 bits—dramatically reducing memory requirements and improving inference speed.

However, traditional quantization methods often result in significant accuracy degradation, especially at lower bit widths. This is where AutoRound makes a difference—it enables high-quality quantization even at ultra-low bit widths (2-4 bits) with minimal performance loss.

What Exactly Is AutoRound?

AutoRound is an advanced quantization library specifically designed for Large Language Models (LLMs) and Vision-Language Models (VLMs). Its core innovation lies in delivering high accuracy at ultra-low bit widths (2-4 bits) with minimal tuning effort. Unlike many other quantization tools that require extensive fine-tuning or specialized hardware knowledge, AutoRound simplifies the process while maintaining excellent performance.

The technology behind AutoRound leverages sign-gradient descent, a mathematical approach that enables efficient optimization of quantization parameters. This allows AutoRound to achieve what many previously considered difficult: maintaining model quality even when reducing precision to just 2-3 bits per weight.

If you’re interested in the technical details, the AutoRound team has published their research on arXiv, which provides a deeper dive into the algorithmic innovations that make this possible.

Why AutoRound Stands Out in the Quantization Landscape

The AI community has developed numerous quantization tools over the years, so what makes AutoRound worth your attention? Let’s examine its distinctive advantages:

1. Exceptional Accuracy at Ultra-Low Bit Widths

AutoRound’s most impressive feature is its ability to maintain high model accuracy even at extremely low bit widths. While many quantization methods struggle to preserve performance below 4 bits, AutoRound delivers strong results at 2-3 bits. You can explore example models in the 2-3 bits collection on Hugging Face.

For 4-bit quantization, AutoRound achieves leading results as demonstrated in the Low Bit Open LLM Leaderboard, which compares various quantization methods across multiple benchmarks.

2. Seamless Integration with Popular AI Frameworks

AutoRound has been designed to work smoothly within existing AI development workflows. It integrates directly with several major frameworks:

• Transformers: Supported in versions 4.51.3 and later
• vLLM: Supported in versions 0.85.post1 and later
• TorchAO: Native compatibility
• sglang: Integration is currently in progress (see PR)

This ecosystem integration means you can incorporate AutoRound into your projects without major workflow disruptions.

3. Multiple Export Format Support

One of AutoRound’s practical advantages is its support for multiple quantization formats, ensuring maximum compatibility across different inference engines:

• AutoRound (default format)
• AutoAWQ
• AutoGPTQ
• GGUF (experimental support)

This flexibility allows you to choose the most appropriate format based on your target deployment environment. For detailed information about supported export formats, you can refer to the export formats documentation.

4. Practical Quantization Costs

Quantization shouldn’t be a time-consuming bottleneck in your development process. AutoRound delivers impressive speed—quantizing a 7B parameter model takes approximately 10 minutes on a single GPU. This efficiency makes it practical for iterative development and experimentation.

For more detailed information about quantization costs under different scenarios, the quantization costs documentation provides comprehensive benchmarks.

5. Comprehensive Vision-Language Model Support

AutoRound isn’t limited to text-only models. It offers out-of-the-box quantization for over 10 vision-language models, making it valuable for multimodal applications. You can explore example quantized VLMs in the VLMs AutoRound collection on Hugging Face.

The support matrix details which specific VLM architectures are compatible with AutoRound.

6. Layerwise Mixed Bits Quantization

Not all layers in a neural network contribute equally to model performance. AutoRound allows you to assign different bit widths to different layers, enabling fine-grained trade-offs between model size and accuracy. This technique, known as layerwise mixed bits quantization, lets you optimize resource usage without unnecessary quality degradation.

For implementation details, check the mixed bits quantization documentation.

7. Round-to-Nearest (RTN) Mode

For scenarios where speed is critical and some accuracy loss is acceptable, AutoRound offers a Round-to-Nearest mode that eliminates the need for calibration data. Activated with the --iters 0 parameter, this mode provides near-instant quantization at the cost of slightly reduced accuracy.

The RTN mode documentation explains how to use this feature effectively.

8. Multiple Quantization Recipes

AutoRound provides three pre-configured quantization approaches to suit different needs:

• auto-round-best: Optimized for maximum accuracy (but 3-5x slower)
• auto-round: Balanced approach for good accuracy with reasonable speed
• auto-round-light: Optimized for speed (2-3x faster) with slightly reduced accuracy at 4-bit and more significant reduction at 2-bit

The quantization recipes documentation offers guidance on selecting the appropriate configuration.

9. Advanced Utility Features

Beyond the core functionality, AutoRound includes several advanced features that enhance its practicality:

• Multiple GPU quantization: Distribute the quantization workload across multiple GPUs
• Multiple calibration datasets: Use combinations of datasets for more robust calibration
• 10+ runtime backend support: Compatibility with various inference engines

Detailed information about these utilities can be found in the step-by-step guide.

10. Ongoing Development for Future Data Types

The AutoRound team continues to expand the library’s capabilities. They’re actively working on supporting additional data types such as MXFP, NVFP, and W8A8, which will further broaden the tool’s applicability.

Recent Developments in AutoRound

AutoRound is an actively maintained project with regular updates. Here are some of the most significant recent developments:

July 2025: GGUF Format Support

AutoRound now offers experimental support for the GGUF format, which is popular for certain inference engines. The team recommends using the optimized Round-to-Nearest (RTN) mode (--iters 0) for all bit widths except 3 bits. Example models demonstrating this capability include:

• Intel/Qwen3-235B-A22B-q2ks-mixed-ar
• Intel/DeepSeek-R1-0528-q2ks-mixed-ar

Future versions (v0.6.1 and beyond) may introduce more advanced algorithms tailored for specific configurations.

May 2025: DeepSeek-R1 Quantization Recipes

The AutoRound team has published specific quantization recipes for the DeepSeek-R1-0528 model, providing optimized configurations for different bit widths:

• Intel/DeepSeek-R1-0528-int2-mixed-ar
• Intel/DeepSeek-R1-0528-int4-ar
• Intel/DeepSeek-R1-0528-int4-asym-ar

These recipes demonstrate how AutoRound can be fine-tuned for specific model architectures.

May 2025: vLLM Integration

AutoRound has been integrated into vLLM, a high-performance LLM serving library. This means you can now run models quantized with AutoRound directly using vLLM versions 0.85.post1 and later, without additional conversion steps.

April 2025: Transformers Integration

Similarly, AutoRound has been integrated into the Hugging Face Transformers library. Models quantized with AutoRound can be loaded and used directly with Transformers versions 4.51.3 and later, simplifying deployment in existing Transformers-based workflows.

March 2025: High-Accuracy INT2 Quantization

The team achieved a significant milestone with the INT2-mixed DeepSeek-R1 model (approximately 200GB in size), which retains 97.9% of the original model’s accuracy. This demonstrates AutoRound’s capability to maintain exceptional performance even at extremely low bit widths. You can explore this model at OPEA/DeepSeek-R1-int2-mixed-sym-inc.

Getting Started with AutoRound

Now that you understand what AutoRound is and why it’s valuable, let’s explore how to install and use it. The process is designed to be straightforward, whether you prefer command-line tools or programmatic API access.

Installation Options

AutoRound offers flexible installation methods to accommodate different hardware environments:

Installing from PyPI

For standard CPU, Intel GPU, or CUDA environments:

pip install auto-round

For HPU (Habana Processing Unit) environments:

pip install auto-round-lib

Building from Source

If you prefer to build from source:

For CPU, Intel GPU, or CUDA environments:

pip install .

For HPU environments:

python setup.py install lib

Command-Line Usage

AutoRound provides a user-friendly command-line interface for quick quantization tasks. Here’s a basic example that quantizes the Qwen3-0.6B model to 4-bit precision:

auto-round \
    --model Qwen/Qwen3-0.6B \
    --bits 4 \
    --group_size 128 \
    --format "auto_gptq,auto_awq,auto_round" \
    --output_dir ./tmp_autoround

This command quantizes the model to 4 bits with a group size of 128 and exports it in multiple formats (AutoGPTQ, AutoAWQ, and AutoRound) to the specified output directory.

Alternative Quantization Recipes

AutoRound offers two additional pre-configured recipes for different scenarios:

For maximum accuracy (but slower processing):

## Best accuracy, 3X slower, low_gpu_mem_usage could save ~20G but ~30% slower
auto-round-best \
  --model Qwen/Qwen3-0.6B \
  --bits 4 \
  --group_size 128 \
  --low_gpu_mem_usage

For faster processing with slightly reduced accuracy:

## Light accuracy, 2-3X speedup, slight accuracy drop at W4 and larger accuracy drop at W2
auto-round-light \
  --model Qwen/Qwen3-0.6B \
  --bits 4 \
  --group_size 128

The general recommendation is to use the standard auto-round configuration for INT4 quantization and auto-round-best for INT2 quantization, but you should adjust based on your specific requirements and available resources.

For Vision-Language Models

When working with vision-language models, use the auto-round-mllm command instead:

auto-round-mllm \
    --model Qwen/Qwen2-VL-2B-Instruct \
    --bits 4 \
    --group_size 128 \
    --output_dir ./tmp_autoround_vlm

API Usage

For more programmatic control, AutoRound provides a Python API:

from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_round import AutoRound

model_name = "Qwen/Qwen3-0.6B"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

bits, group_size, sym = 4, 128, True
autoround = AutoRound(model, tokenizer, bits=bits, group_size=group_size, sym=sym)

output_dir = "./tmp_autoround"
## format= 'auto_round'(default), 'auto_gptq', 'auto_awq'
autoround.quantize_and_save(output_dir, format="auto_round")

This API approach gives you more flexibility to customize the quantization process. For instance, you can adjust parameters like nsamples (number of calibration samples) or iters (optimization iterations) to fine-tune the quantization process.

Advanced API Configuration

For specialized requirements, you can create more customized configurations:

For maximum accuracy (but slower):

# Best accuracy, 4-5X slower, low_gpu_mem_usage could save ~20G but ~30% slower
autoround = AutoRound(model, tokenizer, nsamples=512, iters=1000, 
                      low_gpu_mem_usage=True, bits=bits, 
                      group_size=group_size, sym=sym)

For faster processing:

# 2-3X speedup, slight accuracy drop at W4G128
autoround = AutoRound(model, tokenizer, nsamples=128, iters=50, 
                      lr=5e-3, bits=bits, group_size=group_size, sym=sym)

Quantizing Vision-Language Models via API

The process for quantizing vision-language models is slightly different, requiring additional components:

from auto_round import AutoRoundMLLM
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, AutoTokenizer

## Load the model
model_name = "Qwen/Qwen2-VL-2B-Instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(model_name, 
                                                       trust_remote_code=True, 
                                                       torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)

## Quantize the model
bits, group_size, sym = 4, 128, True
autoround = AutoRoundMLLM(model, tokenizer, processor, 
                         bits=bits, group_size=group_size, sym=sym)
autoround.quantize()

# Save the quantized model
output_dir = "./tmp_autoround"
# Set format='auto_gptq' or 'auto_awq' to use other formats
autoround.save_quantized(output_dir, format="auto_round", inplace=True)

Important Note: If you encounter issues during VLM quantization, try setting iters=0 (to enable RTN mode) and using group_size=32 for better results.

Hyperparameter Reference

Here’s a comprehensive reference for the key hyperparameters you can adjust when using AutoRound:

Parameter	Type	Default	Description
bits	int	4	Number of bits for quantization
group_size	int	128	Size of the quantization group
sym	bool	True	Whether to use symmetric quantization
iters	int	200	Number of tuning iterations
lr	float	None	Learning rate for rounding value (automatically set to 1.0/iters if None)
minmax_lr	float	None	Learning rate for min-max tuning (automatically set to lr if None)
nsamples	int	128	Number of samples for tuning
seqlen	int	2048	Data length of the sequence for tuning
batch_size	int	8	Batch size for training
low_gpu_mem_usage	bool	False	Whether to save GPU memory (at the cost of ~20% more tuning time)
dataset	str/list/tuple/DataLoader	“NeelNanda/pile-10k”	Dataset name for tuning

Using Quantized Models: Inference Examples

Quantization is only half the equation—the real value comes from efficiently using the quantized models. AutoRound supports multiple inference approaches through its ecosystem integrations.

Using vLLM for Inference

vLLM is a high-performance LLM serving library that now supports AutoRound-quantized models:

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}")

Important Note: Support for Mixture-of-Experts (MoE) models and visual language models in vLLM is currently limited.

Using Transformers for Inference

Since AutoRound has been integrated into the Hugging Face Transformers library, you can use the standard Transformers API:

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]))

Critical Warning: 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 devices is also currently limited.

AutoRound automatically selects the best available backend based on the installed libraries and will prompt you to install additional libraries when a better backend is found.

Frequently Asked Questions

How does AutoRound compare to other quantization tools like AutoGPTQ or AutoAWQ?

AutoRound’s primary advantage is its ability to maintain high accuracy at ultra-low bit widths (2-3 bits), where many other tools experience significant performance degradation. It also offers a more streamlined workflow with faster quantization times. Additionally, AutoRound supports exporting to AutoGPTQ and AutoAWQ formats, giving you flexibility in your deployment choices.

What should I do if I encounter memory issues during quantization?

If you run into memory constraints:

1. Use the --low_gpu_mem_usage flag, which can save approximately 20GB of memory but increases processing time by about 30%
2. Reduce the nsamples parameter (number of calibration samples)
3. Decrease the batch_size parameter
4. For particularly large models, consider using the auto-round-light configuration

How do I choose the right quantization parameters for my use case?

The optimal parameters depend on your specific requirements:

• For INT4 quantization: The standard auto-round configuration typically provides the best balance
• For INT2 quantization: Use auto-round-best for maximum accuracy
• If speed is critical: Consider auto-round-light or RTN mode (--iters 0)
• For memory-constrained environments: Try smaller group_size values (e.g., 32 instead of 128)

Why am I getting errors when trying to run my quantized model?

Common issues and solutions:

• Framework version mismatch: Ensure you’re using compatible versions (Transformers 4.51.3+ or vLLM 0.85.post1+)
• Device movement errors: Never manually move the quantized model between devices (e.g., model.to('cpu'))
• Format mismatch: Verify that the quantization format matches what your inference engine expects
• Hardware limitations: Gaudi device support is currently limited

Which model architectures does AutoRound support?

AutoRound supports a wide range of model architectures including:

• Llama
• Qwen
• Mistral
• Mixtral
• DeepSeek
• And numerous vision-language models

For the complete and up-to-date list, check the support matrix.

Can I use AutoRound for commercial applications?

Yes, AutoRound is released under the Apache 2.0 license, which permits commercial use. Always verify the specific terms of the license to ensure compliance with your intended use case.

How does AutoRound handle the trade-off between model size and accuracy?

AutoRound provides several mechanisms to manage this trade-off:

• Bit width selection (2-8 bits)
• Group size configuration
• Mixed precision quantization (different bits per layer)
• Multiple quantization recipes optimized for different priorities

The key is to start with the highest bit width that meets your size constraints, then adjust other parameters to optimize for your specific accuracy requirements.

What’s the difference between symmetric and asymmetric quantization?

Symmetric quantization uses the same scale factor for positive and negative values, which is computationally efficient but may not capture data distribution nuances as well. Asymmetric quantization uses different scale factors, potentially preserving more information at the cost of slightly more complex computation. AutoRound defaults to symmetric quantization (sym=True), but you can switch to asymmetric by setting sym=False.

How important is the calibration dataset for quantization quality?

The calibration dataset plays a crucial role in quantization quality. AutoRound uses the NeelNanda/pile-10k dataset by default, but for best results, you should use a dataset that closely matches your target application domain. You can specify custom datasets using the dataset parameter, which accepts local JSON files or combinations of datasets.

Can I quantize only specific parts of a model?

Yes, AutoRound supports layerwise configuration through the layer_config parameter. This allows you to apply different quantization settings to different parts of the model, which is particularly useful when certain layers are more sensitive to precision loss than others.

Looking Ahead: The Future of AutoRound

The AutoRound development team continues to push the boundaries of what’s possible in model quantization. Beyond the current weight-only quantization capabilities, they’re actively expanding support for additional data types such as MXFP, NVFP, and W8A8. These advancements will further broaden AutoRound’s applicability across different hardware platforms and use cases.

As AI models continue to grow in size and complexity, tools like AutoRound become increasingly important for making these powerful technologies accessible in real-world applications. The ability to deploy high-quality models on resource-constrained devices opens up new possibilities for edge AI, mobile applications, and cost-effective cloud deployments.

Conclusion: Why AutoRound Matters

Model quantization isn’t just a technical optimization—it’s an essential enabler for practical AI deployment. Without effective quantization tools, many of the AI applications we take for granted today would be prohibitively expensive or simply impossible to implement.

AutoRound stands out by making high-quality quantization accessible and efficient. Its ability to maintain model accuracy at ultra-low bit widths, combined with its straightforward integration into existing workflows, makes it a valuable tool for AI practitioners at all levels.

Whether you’re a researcher exploring the limits of model compression, an engineer optimizing deployment costs, or a developer bringing AI capabilities to resource-constrained environments, AutoRound provides a powerful solution that balances performance, efficiency, and ease of use.

The open-source nature of AutoRound, released under the permissive Apache 2.0 license, ensures that these benefits are available to the entire AI community. As the project continues to evolve with new features and capabilities, it’s poised to play an increasingly important role in the AI ecosystem.

If you find AutoRound valuable for your work, consider showing your support by starring the repository on GitHub and sharing it with your professional network. This helps the development team continue improving the tool and expanding its capabilities for the benefit of the entire AI community.

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This article is based exclusively on information from the AutoRound documentation and source code. All technical details, code examples, and model references are drawn directly from the official AutoRound repository and associated Hugging Face resources.