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REAL-Q Quantizes LLMs with Dynamic Gradient Descent

Qian Zhang, Yaoming Li, Zhewen Tan, Yanshu Wang, Heng Lu, Kun Su, Zongwei Lv, Wenhan Yu, Yongge Ma, Yinjun Han, Ruikuang Liu, Tong Yang· September 2, 2026 View original

Key takeaways

  • REAL-Q is a novel PTQ method for LLMs that improves quantization quality.
  • It uses dynamic Block-wise Gradient Descent to address information misalignment and error propagation.
  • REAL-Q significantly reduces KL divergence compared to other state-of-the-art methods.
  • This approach enables more efficient deployment of LLMs on resource-constrained devices.

Who benefits

Edge AIMobile ComputingCloud ComputingAI/ML DevelopmentAutomotive

Summary

REAL-Q is a novel post-training quantization (PTQ) paradigm for LLMs that uses dynamic Block-wise Gradient Descent to mitigate error propagation. It targets an end-to-end aligned surrogate of the global loss, significantly reducing KL divergence compared to state-of-the-art methods on LLaMA-3.1 and Qwen3.

This paper introduces REAL-Q (Real-time E2E-loss Aligned LLM Quantization), a new paradigm for post-training quantization (PTQ) of large language models (LLMs). PTQ is crucial for deploying LLMs on resource-constrained hardware. Traditional state-of-the-art PTQ methods often approximate the global loss and freeze the Hessian across entire layers, leading to "information misalignment" as the loss landscape shifts. REAL-Q aims to overcome these limitations. Instead of simplifying the objective for analytical tractability, REAL-Q directly targets an end-to-end aligned surrogate of the global loss. It refines this objective using a fine-grained, dynamic Block-wise Gradient Descent applied after every column block (128 columns). This approach, coupled with a sliding window mechanism for smooth cross-layer transitions, effectively mitigates error propagation throughout the network. Experiments on LLaMA-3.1 (8B and 70B) and Qwen3 (0.6B-32B) at W4A16 demonstrate that REAL-Q reduces end-to-end KL divergence by up to 49% relative to existing globally-guided methods, indicating a significant improvement in quantization quality.

Why it matters

For professionals deploying LLMs, REAL-Q offers a method to achieve much more efficient model sizes and faster inference without sacrificing significant accuracy, making advanced LLMs more accessible for edge devices and cost-sensitive applications.

How to implement this in your domain

  1. 1Investigate REAL-Q as a potential post-training quantization method for deploying LLMs on resource-constrained hardware.
  2. 2Benchmark REAL-Q against current quantization techniques to assess its performance and efficiency gains for your specific models.
  3. 3Explore integrating dynamic gradient descent and block-wise refinement into your LLM optimization pipelines.
  4. 4Consider the trade-offs between quantization quality and computational overhead during deployment planning.

Original post by Qian Zhang, Yaoming Li, Zhewen Tan, Yanshu Wang, Heng Lu, Kun Su, Zongwei Lv, Wenhan Yu, Yongge Ma, Yinjun Han, Ruikuang Liu, Tong Yang

"arXiv:2609.00049v1 Announce Type: new Abstract: Post-training quantization (PTQ) is essential for deploying large language models (LLMs) under strict resource constraints. State-of-the-art PTQ methods quantize each layer with a single closed-form second-order solver: to remain an…"

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Originally posted by Qian Zhang, Yaoming Li, Zhewen Tan, Yanshu Wang, Heng Lu, Kun Su, Zongwei Lv, Wenhan Yu, Yongge Ma, Yinjun Han, Ruikuang Liu, Tong Yang on X · view source

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