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Title: GitHub - intel/auto-round: A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers. · GitHub

Open Graph Title: GitHub - intel/auto-round: A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers.

X Title: GitHub - intel/auto-round: A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers.

Description: A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers. - intel/auto-round

Open Graph Description: A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers....

X Description: A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers....

Opengraph URL: https://github.com/intel/auto-round

X: @github

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Apache-2.0 licensehttps://github.com/intel/auto-round
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https://github.com/intel/auto-round/blob/main/docs/imgs/AutoRound.png
https://github.com/intel/auto-round#-advanced-quantization-algorithm-for-llms
https://github.com/intel/auto-round
https://github.com/intel/auto-round/releases
https://pypi.org/project/auto-round-nightly
https://github.com/intel/auto-round/blob/main/LICENSE
https://huggingface.co/Intel
简体中文https://github.com/intel/auto-round/blob/main/README_CN.md
User Guidehttps://github.com/intel/auto-round/blob/main/docs/step_by_step.md
用户指南https://github.com/intel/auto-round/blob/main/docs/step_by_step_CN.md
https://github.com/intel/auto-round#-what-is-autoround
SignRoundV1https://arxiv.org/pdf/2309.05516
SignRoundV2http://arxiv.org/abs/2512.04746
User Guidehttps://github.com/intel/auto-round/blob/main/docs/step_by_step.md
https://github.com/intel/auto-round/blob/main/docs/imgs/autoround_overview.png
https://github.com/intel/auto-round#-whats-new
AutoScheme Accuracyhttps://github.com/intel/auto-round/blob/main/docs/auto_scheme_acc.md
vLLM bloghttps://vllm.ai/blog/2026-06-02-vllm-omni-autoround
Intel Low Bit Open LLM Leaderboardhttps://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard
Dochttps://github.com/intel/auto-round/blob/main/docs/step_by_step.md#model-free-mode
PRhttps://github.com/intel/auto-round/pull/1526
Paperhttp://arxiv.org/abs/2512.04746
Notes for evaluating LLaMA modelshttps://github.com/intel/auto-round/blob/main/docs/alg_202508.md
Usagehttps://github.com/vllm-project/llm-compressor/tree/main/examples/autoround/README.md
vLLM bloghttps://blog.vllm.ai/2025/12/09/intel-autoround-llmc.html
RedHat bloghttps://developers.redhat.com/articles/2025/12/09/advancing-low-bit-quantization-llms-autoround-x-llm-compressor
X posthttps://x.com/vllm_project/status/1998710451312771532
Intel bloghttps://community.intel.com/t5/Blogs/Products-and-Solutions/HPC/Advancing-Low-Bit-Quantization-for-LLMs-AutoRound-x-LLM/post/1729336
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LMSYS Bloghttps://lmsys.org/blog/2025-11-13-AutoRound/
X posthttps://x.com/lmsysorg/status/1991977019220148650?s=20
Intel bloghttps://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/AutoRound-Meets-SGLang-Enabling-Quantized-Model-Inference-with/post/1727196
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https://github.com/intel/auto-round#-key-features
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quantization costshttps://github.com/intel/auto-round/blob/main/docs/step_by_step.md#quantization-costs
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multiple gpus quantizationhttps://github.com/intel/auto-round/blob/main/docs/step_by_step.md#devicemulti-gpu-setting-in-quantization
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https://github.com/intel/auto-round#publications--events
SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMshttps://arxiv.org/abs/2512.04746
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMhttps://aclanthology.org/2024.findings-emnlp.662/
TEQ: Trainable Equivalent Transformation for Quantization of LLMshttps://arxiv.org/abs/2310.10944
Effective Post-Training Quantization for Large Language Modelshttps://medium.com/intel-analytics-software/effective-post-training-quantization-for-large-language-models-with-enhanced-smoothquant-approach-93e9d104fb98
Full Publication Listhttps://github.com/intel/auto-round/blob/main/docs/publication_list.md
https://github.com/intel/auto-round#acknowledgement
https://github.com/intel/auto-round#-support-us
transformers https://github.com/topics/transformers
rounding https://github.com/topics/rounding
quantization https://github.com/topics/quantization
omni https://github.com/topics/omni
int4 https://github.com/topics/int4
diffusers https://github.com/topics/diffusers
llms https://github.com/topics/llms
vllm https://github.com/topics/vllm
gguf https://github.com/topics/gguf
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mxfp4 https://github.com/topics/mxfp4
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Report repository https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fintel%2Fauto-round&report=intel+%28user%29
Releases 34https://github.com/intel/auto-round/releases
V0.13.1 patch release Latest Jun 12, 2026 https://github.com/intel/auto-round/releases/tag/v0.13.1
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