# AutoRound 与 SGLang 正式集成，实现低比特量化模型高效推理

- 来源：LMSYS：Blog（Chatbot Arena 团队）
- 发布时间：2025-11-14 00:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnxbjke5006ysln03qpa0db1
- 原文链接：https://www.lmsys.org/blog/2025-11-13-AutoRound

## AI 摘要

AutoRound 与 SGLang 宣布合作，支持 INT2-INT8 低比特量化模型的高效推理部署。基于符号梯度优化算法，AutoRound 在 INT2 精度下准确率较主流基线提升 2.1 倍，单 GPU 量化 72B 模型仅需 37 分钟。开发者可将 GPTQ、AWQ 或 GGUF 格式的量化模型直接部署至 SGLang v0.5.4.post2+，兼容 LLM、VLM 及 MoE 架构，在最小精度损失下显著降低推理延迟。

## 正文

Contents

Overview

What Is AutoRound?

AutoRound Highlights

Integration Overview

1. Quantize with AutoRound

1.1 API Usage

1.2 CMD Usage

2. Deploying with SGLang

2.1 OpenAI-Compatible Inference Usage

2.2 Offline Engine API Inference Usage

Quantization Roadmap

Conclusion

🚀 AutoRound Meets SGLang: Enabling Quantized Model Inference with AutoRound

Overview

We are thrilled to announce an official collaboration between SGLang and AutoRound, enabling low-bit quantization for efficient LLM inference.

Through this integration, developers can now quantize large models with AutoRound’s signed-gradient optimization and directly deploy them in SGLang’s efficient runtime, achieving low-bit model inference with minimal accuracy loss and significant latency reduction.

What Is AutoRound?

AutoRound is an advanced post-training quantization (PTQ) toolkit designed for Large Language Models (LLMs) and Vision-Language Models (VLMs). It uses signed gradient descent to jointly optimize weight rounding and clipping ranges, enabling accurate low-bit quantization (e.g., INT2 - INT8) with minimal accuracy loss in most scenarios. For example, at INT2 precision, it outperforms popular baselines by up to 2.1x higher in relative accuracy. At INT4 precision, AutoRound continues to hold a competitive edge in most cases. The image below provides an overview of the core algorithm in AutoRound.

Full technical details are presented in the AutoRound paper:

👉 Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

AutoRound algorithm overview

Despite its robust performance, AutoRound remains fast and lightweight—quantizing a 72B model takes only 37 minutes on a single GPU under light mode.

It further supports mixed-bit tuning, lm-head quantization, GPTQ/AWQ/GGUF format exports, and customizable tuning recipes.

AutoRound Highlights

AutoRound is not only focused on algorithmic innovation and exploration, but also widely recognized for its completeness in quantization engineering.

Accuracy: deliver superior accuracy at low-bit precision

Average accuracy of 10+ tasks at INT4 weight

Schemes: support weight-only quantization, weight & activation quantization, dynamic and static for activation quantization

Mixed-bits: propose an effective algorithm to generate mixed-bits / other data types schemes in minutes

Broad Compatibility: Support nearly all popular LLM architectures and over 10 vision-language models (VLMs) Support Devices: CPU, Intel GPU, CUDA Support Data Types: INT2 - INT8, MXFP4, NVFP4, FP8, and MXFP8

Support nearly all popular LLM architectures and over 10 vision-language models (VLMs)

Support Devices: CPU, Intel GPU, CUDA

Support Data Types: INT2 - INT8, MXFP4, NVFP4, FP8, and MXFP8

Efficiency: Enables block-wise tuning to lower VRAM usage without sacrificing throughput yet fast

Quantization time cost comparison

Community adoption: Work seamlessly with SGLang, TorchAO, Transformers, and vLLM Widely used by HuggingFace model hubs such as Intel, OPEA, Kaitchup, and fbaldassarri with approximately two million downloads

Work seamlessly with SGLang, TorchAO, Transformers, and vLLM

Widely used by HuggingFace model hubs such as Intel, OPEA, Kaitchup, and fbaldassarri with approximately two million downloads

Export Formats: AutoRound GPTQ AWQ GGUF Compressed-tensor (initial support)

AutoRound

GPTQ

AWQ

GGUF

Compressed-tensor (initial support)

Integration Overview

SGLang provides a next-generation inference runtime built for scalable, low-latency LLM deployment. Its multi-modal, multi-GPU, and streaming execution model enables both chat and agentic reasoning workloads with exceptional efficiency.

SGLang’s flexible architecture now offers native hooks for quantized model loading, unlocking AutoRound’s full potential for deployment.

1. Quantize with AutoRound

AutoRound automatically optimizes weight rounding and exports quantized weights that compatible with SGLang.

# for LLM from auto_round import AutoRound model_id = "meta-llama/Llama-3.2-1B-Instruct" quant_path = "Llama-3.2-1B-Instruct-autoround-4bit" # Scheme examples: "W2A16", "W3A16", "W4A16", "W8A16", "NVFP4", "MXFP4" (no real kernels), "GGUF:Q4_K_M", etc. scheme = "W4A16" format = "auto_round" autoround = AutoRound(model_id, scheme=scheme) autoround.quantize_and_save(quant_path, format=format) # quantize and save

# for LLM from auto_round import AutoRound model_id = "meta-llama/Llama-3.2-1B-Instruct" quant_path = "Llama-3.2-1B-Instruct-autoround-4bit" # Scheme examples: "W2A16", "W3A16", "W4A16", "W8A16", "NVFP4", "MXFP4" (no real kernels), "GGUF:Q4_K_M", etc. scheme = "W4A16" format = "auto_round" autoround = AutoRound(model_id, scheme=scheme) autoround.quantize_and_save(quant_path, format=format) # quantize and save

auto-round \ --model Qwen/Qwen2-VL-2B-Instruct \ --bits 4 \ --group_size 128 \ --format "auto_round" \ --output_dir ./tmp_autoround

auto-round \ --model Qwen/Qwen2-VL-2B-Instruct \ --bits 4 \ --group_size 128 \ --format "auto_round" \ --output_dir ./tmp_autoround

2. Deploying with SGLang

SGLang supports AutoRound-quantized models directly (Version>=v0.5.4.post2). It is compatible with SGLang-supported modeling architectures, including common LLM, VLM, and MoE models, and also supports inference and evaluation of AutoRound mixed-bit quantized models.

from sglang.test.doc_patch import launch_server_cmd from sglang.utils import wait_for_server, print_highlight, terminate_process # This is equivalent to running the following command in your terminal # python3 -m sglang.launch_server --model-path Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound --host 0.0.0.0 server_process, port = launch_server_cmd( """ python3 -m sglang.launch_server --model-path Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound \ --host 0.0.0.0 --log-level warning """ ) wait_for_server(f"http://localhost:{port}")

from sglang.test.doc_patch import launch_server_cmd from sglang.utils import wait_for_server, print_highlight, terminate_process # This is equivalent to running the following command in your terminal # python3 -m sglang.launch_server --model-path Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound --host 0.0.0.0 server_process, port = launch_server_cmd( """ python3 -m sglang.launch_server --model-path Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound \ --host 0.0.0.0 --log-level warning """ ) wait_for_server(f"http://localhost:{port}")

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']}")

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']}")

More flexible configurations and deployment options are waiting for you to explore!

Quantization Roadmap

AutoRound’s quantization benchmark results demonstrate robust accuracy retention at low precision. The results below highlight AutoRound’s strong advantages and potential in MXFP4, NVFP4, and mixed-bits model quantization. Note that the accuracy result is measured by average accuracy across lambada_openai, hellaswag, piqa, winogrande, and mmlu task.

As part of AutoRound roadmap, we plan to continue enhancing MXFP4 & NVFP4 accuracy for common models and auto mixed-bits quantization in the upcoming releases.

MXFP4 & NVFP4 Quantization. RTN (Round-to-nearest) algorithm is baseline, and 'alg_ext' option indicates experimental optimization algorithms enabled. mxfp4llama3.1-8B-InstructQwen2-7.5-InstructPhi4Qwen3-32BRTN0.62120.65500.71670.6901AutoRound0.66860.67580.72470.7211AutoRound+alg_ext0.67320.68090.72250.7201 nvfp4llama3.1-8B-InstructQwen2-7.5-InstructPhi4Qwen3-32BRTN0.68760.69060.72960.7164AutoRound0.69180.69730.73060.7306AutoRound+alg_ext0.69650.69890.73180.7295

MXFP4 & NVFP4 Quantization. RTN (Round-to-nearest) algorithm is baseline, and 'alg_ext' option indicates experimental optimization algorithms enabled.

mxfp4llama3.1-8B-InstructQwen2-7.5-InstructPhi4Qwen3-32BRTN0.62120.65500.71670.6901AutoRound0.66860.67580.72470.7211AutoRound+alg_ext0.67320.68090.72250.7201

nvfp4llama3.1-8B-InstructQwen2-7.5-InstructPhi4Qwen3-32BRTN0.68760.69060.72960.7164AutoRound0.69180.69730.73060.7306AutoRound+alg_ext0.69650.69890.73180.7295

Auto MXFP4 & MXFP8 Mixed-Bits Quantization Average bitsLlama3.1-8B-IQwen2.5-7B-IQwen3-8BQwen3-32BBF160.7076 (100%)0.7075 (100%)0.6764 (100%)0.7321 (100%)4-bit0.6626 (93.6%)0.6550 (92.6%)0.6316 (93.4%)0.6901 (94.3%)4.5-bit0.6808 (96.2%)0.6776 (95.8%)0.6550 (96.8%)0.7176 (98.0%)5-bit0.6857 (96.9%)0.6823 (96.4%)0.6594 (97.5%)0.7201 (98.3%)6-bit0.6975 (98.6%)0.6970 (98.5%)0.6716 (99.3%)0.7303 (99.8%)

Auto MXFP4 & MXFP8 Mixed-Bits Quantization

Average bitsLlama3.1-8B-IQwen2.5-7B-IQwen3-8BQwen3-32BBF160.7076 (100%)0.7075 (100%)0.6764 (100%)0.7321 (100%)4-bit0.6626 (93.6%)0.6550 (92.6%)0.6316 (93.4%)0.6901 (94.3%)4.5-bit0.6808 (96.2%)0.6776 (95.8%)0.6550 (96.8%)0.7176 (98.0%)5-bit0.6857 (96.9%)0.6823 (96.4%)0.6594 (97.5%)0.7201 (98.3%)6-bit0.6975 (98.6%)0.6970 (98.5%)0.6716 (99.3%)0.7303 (99.8%)

Conclusion

The integration of AutoRound and SGLang marks a major milestone in efficient AI model deployment. This collaboration bridges precision optimization and runtime scalability, allowing developers to move seamlessly from quantization to real-time inference with minimal friction. AutoRound’s signed-gradient quantization ensures high fidelity even at extreme compression ratios, while SGLang’s high-throughput inference engine unlocks the full potential of low-bit execution across CPUs, GPUs, and multi-node clusters.

Looking forward, we aim to expand support for advanced quantization formats, optimize kernel efficiency, and bring AutoRound quantization into** **broader multimodal and agentic workloads. Together, AutoRound and SGLang are setting a new standard for intelligent, efficient, and scalable LLM deployment.