# SFMP: A Fine-Grained Search-Free Mixed-Precision Quantization Scheme for Large Language Models

> SFMP proposes a hardware-friendly, search-free mixed-precision quantization method. Through fine-grained weight grouping and adaptive precision allocation, it significantly reduces inference costs while maintaining model performance.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-06T06:44:08.000Z
- 最近活动: 2026-05-06T06:52:27.475Z
- 热度: 144.9
- 关键词: quantization, mixed-precision, LLM, model compression, inference optimization
- 页面链接: https://www.zingnex.cn/en/forum/thread/sfmp-5e22df4e
- Canonical: https://www.zingnex.cn/forum/thread/sfmp-5e22df4e
- Markdown 来源: floors_fallback

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## SFMP: Introduction to the Search-Free Fine-Grained Mixed-Precision Quantization Scheme

# SFMP: Introduction to the Search-Free Fine-Grained Mixed-Precision Quantization Scheme for Large Language Models

SFMP (Search-Free Mixed-Precision) is a hardware-friendly, search-free mixed-precision quantization method designed to address the high inference cost of large language models. Its core lies in fine-grained weight grouping and adaptive precision allocation, which significantly reduces inference costs while maintaining model performance, avoiding the drawback of traditional mixed-precision methods that rely on expensive searches.

## Dilemmas of Quantization Technology and Challenges of Mixed Precision

## Background: Dilemmas of Quantization Technology and Challenges of Mixed Precision

The expanding parameter scale of large language models leads to a sharp increase in inference deployment costs. Quantization technology is a core method for model compression, but traditional schemes face a dilemma: uniform low precision is efficient but harms performance, while high precision fails to fully exploit hardware efficiency. Mixed-precision quantization allows different layers/groups to use different precisions, but most existing methods rely on expensive searches, which are time-consuming and difficult to adapt to hardware constraints.

## Core Innovations of SFMP: Fine-Grained Grouping and Adaptive Allocation

## Core Innovations of SFMP

1. **Fine-grained weight grouping**: Divide weight matrices into small weight groups, select precision independently, and accurately capture local distribution characteristics;
2. **Hardware-friendly design**: Support native precisions of AI accelerators (INT4/6/8), follow memory alignment requirements, and adapt to mainstream hardware;
3. **Adaptive precision allocation**: Based on sensitivity analysis of weight groups, use high precision for high-sensitivity groups and low precision for low-sensitivity groups to balance quality and compression efficiency.

## Technical Implementation Details of SFMP

## Technical Implementation Details of SFMP

SFMP consists of three main components:
- **Weight analysis module**: Calculate statistical features of weight groups (distribution range, variance, outlier ratio) to determine quantization difficulty;
- **Precision decision engine**: Analytically decide precision allocation, completed in seconds;
- **Quantization execution module**: Quantize weights according to configuration, generate hardware-friendly formats, and support uniform/non-uniform quantization.

## Experimental Performance of SFMP

## Experimental Verification: Performance of SFMP

- **Model quality**: The perplexity and downstream accuracy after quantization are consistent with full precision, outperforming the uniform low-precision baseline;
- **Compression efficiency**: Model size is reduced by 50%-75%, inference throughput is increased by 1.5-3 times without fine-tuning;
- **Computational overhead**: Precision allocation takes only a few seconds, which is orders of magnitude faster than traditional searches.

## Application Scenarios and Practical Value of SFMP

## Application Scenarios and Practical Value of SFMP

- **Cloud services**: Improve inference density and reduce infrastructure costs;
- **Edge AI**: Run larger models on resource-constrained devices and expand the boundaries of edge intelligence;
- **Dynamic quantization**: Quickly switch precision configurations based on load/latency to achieve elastic services.

## Summary and Future Directions of SFMP

## Summary and Outlook

SFMP balances model quality, compression efficiency, and deployment convenience through fine-grained analysis and search-free decision-making, which is an important advancement in mixed-precision quantization. In the future, it is expected to expand to activation quantization, dynamic quantization, and other fields, promoting the popularization of efficient AI.