AI领域的“瘦身术”：八位量化，让大模型也能“轻装上阵”
随着人工智能技术的飞速发展，AI模型变得越来越强大，能够完成的任务也越来越复杂。然而，这背后往往伴随着一个“甜蜜的负担”：模型规模的指数级增长。动辄数十亿甚至上万亿的参数，让这些“AI巨兽”如同吞金兽一般，对计算资源、存储空间和运行速度提出了极高的要求。这不仅限制了AI在手机、智能音箱等边缘设备上的普及，也让大型模型部署和运行的成本居高不下。
正是在这样的背景下，一种名为“八位量化”（8-bit Quantization）的技术应运而生，它就像AI模型的“瘦身术”，在不大幅牺牲性能的前提下，让这些庞大的模型也能“轻装上阵”，飞入寻常百姓家。
什么是“量化”？——数字世界的“精度”调节阀
在解释“八位量化”之前，我们先来理解一下什么是“量化”。
想象一下，你有一个非常大的调色板，里面包含了数百万种微妙的色彩（就像专业摄影师使用的那种）。如果你想把一幅用这种调色板创作的画作发送给朋友，但只允许使用一个非常小的调色板（比如只有256种颜色），你该怎么办？你会尝试用这256种最能代表原画的颜色来近似表现所有的细节。这个把“数百万种颜色”简化为“256种颜色”的过程，就是一种“量化”。
在AI领域，这个“颜色”就是模型内部进行计算和表示的数值，比如权重（模型学习到的知识）和激活值（模型处理数据时的中间结果）。计算机通常使用一种叫做“浮点数”（Float）的表示方式来存储这些数值，其中最常用的是32位浮点数（FP32），它能提供非常高的精度，就像拥有数百万种颜色的调色板。这里的“位”（bit）可以理解为表示一个数字所使用的“空间大小”或“细节等级”。32位就像用32个小格子来记录一个数字，所以它能表达的范围和精度都非常高。
“量化”的本质，就是将这些高精度的浮点数（如32位浮点数、16位浮点数）转换为低精度的整数（如8位整数或更低）的过程。
聚焦八位量化：从“细致描绘”到“精准速写”
那么，“八位量化”具体指的是什么呢？
顾名思义，它特指将原本用32位浮点数（或者16位浮点数）表示的数值，映射并转换为用8位整数来表示。8位整数能表示的数值范围通常是-128到127，或者0到255（共有256种可能）。
我们再用一个比喻来理解：
如果你要描绘一片树叶的细节，用32位浮点数就像是使用一把极为精密的游标卡尺，能精确测量到小数点后很多位，细致到连叶片上最微小的绒毛都能刻画出来。而使用8位整数，就像换成了一把普通的刻度尺，虽然无法测量到毫米以下的微小差距，但对于把握叶片的整体形状、大小和主要纹理来说，已经足够了。在这个转换过程中，尽管一些“微不足道”的细节会被“舍弃”（近似处理），但叶片的整体识别度仍然很高。
其核心原理可以概括为：
通过找到一个缩放因子（scale）和零点（zero-point），将原来大范围、连续变化的浮点数，线性地映射到8位整数能够表示的有限、离散的范围内，并进行四舍五入和截断处理。
八位量化的“三大利器”：轻、快、省
将AI模型的数值从32位浮点数量化到8位整数，带来的好处是显而易见的，主要体现在以下三个方面：
- 模型更小巧（轻）：每个数值从占用4字节（32位）变为占用1字节（8位），模型体积直接缩小了四倍！这就像把一部2小时的高清电影压缩成了标清版本，下载、传输和存储都变得更加便捷。对于需要部署在手机、智能家居等存储空间有限的边缘设备上的AI模型来说，这一点至关重要。例如，一个700亿参数的大模型如果使用32位浮点数表示，可能需要非常大的内存，而量化后会大幅减少，降低部署成本。
- 运算更迅捷（快）：计算机处理整数运算通常比处理浮点运算要快得多，尤其是现代处理器为8位整数运算提供了专门的加速指令（如NVIDIA的Tensor Core支持INT8运算）。这意味着模型在执行推理（即根据输入数据生成结果）时，速度会显著提升。对于自动驾驶、实时语音识别等对响应速度要求极高的应用场景，秒级的延迟优化都能带来更好的用户体验。
- 能耗更经济（省）：更小的模型体积意味着更少的内存读取带宽需求，更快的运算速度则减少了处理器的工作时间。这些都直接带来了更低的能源消耗。在移动设备和物联网设备上，这有助于延长电池续航时间，降低设备的运行成本。
因此，八位量化成为了解决AI模型“大胃王”问题，推动AI技术普惠化发展的关键技术之一。
鱼与熊掌的抉择：精度与效率的平衡
当然，任何技术都不是完美的，八位量化也不例外。将高精度数据转换为低精度数据，不可避免地会带来一定的精度损失。在某些对精度要求极高的AI任务中，这种损失可能会影响模型的表现。就像把高清照片压缩成标清照片，虽然大部分细节还在，但放大后可能会发现一些模糊。
为了最大限度地减少这种精度损失，研究人员开发了多种技术：
- 训练后量化（Post-Training Quantization, PTQ）：在模型训练完成后直接进行量化。这种方法简单快速，但可能对模型精度有一定影响。
- 量化感知训练（Quantization-Aware Training, QAT）：在模型训练过程中就模拟量化带来的影响，让模型提前“适应”低精度环境。这种方法通常能获得更好的精度表现，但需要重新训练模型，计算成本较高。
- 混合精度量化：对模型中不同敏感度的部分采用不同的精度，例如，对对精度要求高的层保留更高的精度（如16位），而其他部分进行8位量化，以在性能和精度之间找到最佳平衡。
八位量化的“星辰大海”：应用与未来
八位量化技术已经被广泛应用于图像识别、语音识别和自然语言处理等领域。特别是在近年来爆发式发展的大语言模型（LLM）领域，八位量化发挥了举足轻重的作用。例如，LLM.int8()这样的量化方法，能够让原本在消费级硬件上难以运行的巨型模型，也能在更少的GPU显存下高效执行推理任务。
最新进展和应用案例印证了这一点：
有研究指出，2024年的AI模型量化技术正经历从实验室到产业大规模应用的关键转型，从INT4到更极端低比特量化的突破、自动化量化工具链的成熟、专用硬件与量化算法的协同优化等成为核心趋势。例如，浪潮信息发布的源2.0-M32大模型4位和8位量化版，其性能可媲美700亿参数的LLaMA3开源大模型，但4位量化版推理运行显存仅需23.27GB，是LLaMA3-70B显存的约1/7。
未来，随着硬件对低精度计算支持的不断完善以及量化算法的持续优化，我们不仅会看到更普遍的8位量化，甚至4位量化（INT4）甚至更低比特的量化技术也将成为主流。届时，AI模型的部署将更加灵活，运行将更加高效，为AI技术的普及和创新应用打开更广阔的空间。
结语
八位量化就像一座桥梁，连接了高性能AI模型与受限的计算资源，让原本“高不可攀”的AI技术变得“触手可及”。它不仅降低了AI的部署和运行成本，提升了推理速度和能效，更是推动AI向移动端、边缘设备普及的关键一步。通过这种巧妙的“瘦身术”，我们期待AI技术能够更好地服务于每一个人，在数字世界的各个角落绽放光芒。
The “Slimming Technique” of AI: 8-bit Quantization, Enabling Large Models to “Pack Light”
With the rapid development of artificial intelligence technology, AI models are becoming increasingly powerful and capable of completing more complex tasks. However, this often comes with a “sweet burden”: the exponential growth of model scale. Billions or even trillions of parameters make these “AI behemoths” like gold-swallowing beasts, placing extremely high demands on computing resources, storage space, and running speed. This not only limits the popularity of AI on edge devices such as mobile phones and smart speakers but also keeps the cost of deploying and running large models high.
Against this backdrop, a technology named “8-bit Quantization” has emerged. It is like a “slimming technique” for AI models, allowing these huge models to “pack light” and fly into ordinary households without significantly sacrificing performance.
What is “Quantization”? — The “Precision” Regulator of the Digital World
Before explaining “8-bit Quantization,” let’s first understand what “quantization” is.
Imagine you have a very large palette containing millions of subtle colors (like the ones used by professional photographers). If you want to send a painting created with this palette to a friend but are only allowed to use a very small palette (say, with only 256 colors), what would you do? You would try to use these 256 colors that best represent the original painting to approximate all the details. This process of simplifying “millions of colors” into “256 colors” is a form of “quantization.”
In the AI field, this “color” is the numerical value used for internal calculation and representation in the model, such as weights (knowledge learned by the model) and activation values (intermediate results when the model processes data). Computers usually use a representation method called “Floating Point” (Float) to store these values, with 32-bit Floating Point (FP32) being the most common. It provides very high precision, like a palette with millions of colors. Here, “bit” can be understood as the “space size” or “detail level” used to represent a number. 32-bit is like using 32 small boxes to record a number, so the range and precision it can express are very high.
The essence of “quantization” is the process of converting these high-precision floating-point numbers (such as 32-bit floats, 16-bit floats) into low-precision integers (such as 8-bit integers or lower).
Focusing on 8-bit Quantization: From “Detailed Depiction” to “Precise Sketching”
So, what exactly does “8-bit Quantization” refer to?
As the name suggests, it specifically refers to mapping and converting numerical values originally represented by 32-bit floating-point numbers (or 16-bit floating-point numbers) into 8-bit integers. An 8-bit integer can typically represent a range of values from -128 to 127, or 0 to 255 (a total of 256 possibilities).
Let’s use another analogy to understand:
If you want to depict the details of a leaf, using a 32-bit floating-point number is like using an extremely precise vernier caliper, capable of measuring to many decimal places, detailed enough to portray even the tiniest fuzz on the leaf blade. Using an 8-bit integer is like switching to an ordinary ruler. Although it cannot measure minute differences below a millimeter, it is sufficient for grasping the overall shape, size, and main texture of the leaf. In this conversion process, although some “insignificant” details will be “discarded” (approximated), the overall recognizability of the leaf remains high.
Its core principle can be summarized as:
By finding a scaling factor (scale) and a zero-point, the original large-range, continuously changing floating-point numbers are linearly mapped to the limited, discrete range that 8-bit integers can represent, followed by rounding and truncation.
The “Three Sharp Weapons” of 8-bit Quantization: Light, Fast, and Economical
Quantizing AI model values from 32-bit floating-point to 8-bit integer brings obvious benefits, mainly reflected in the following three aspects:
- More Compact Models (Light): Each value changes from occupying 4 bytes (32 bits) to occupying 1 byte (8 bits), directly reducing the model size by four times! This is like compressing a 2-hour HD movie into a SD version, making downloading, transmission, and storage much more convenient. This is crucial for AI models that need to be deployed on edge devices with limited storage space, such as mobile phones and smart home devices. For example, a large model with 70 billion parameters might require huge memory if represented by 32-bit floating-point numbers, but quantization will drastically reduce it, lowering deployment costs.
- Faster Computation (Fast): Computers usually process integer operations much faster than floating-point operations, especially modern processors that provide specialized acceleration instructions for 8-bit integer operations (such as NVIDIA’s Tensor Core supporting INT8 operations). This means that when the model performs inference (i.e., generating results based on input data), the speed will be significantly improved. For application scenarios requiring extremely high response speeds, such as autonomous driving and real-time voice recognition, even millisecond-level latency optimization can bring a better user experience.
- More Economical Energy Consumption (Economical): Smaller model size means less memory read bandwidth demand, and faster calculation speed reduces processor working time. These directly lead to lower energy consumption. On mobile devices and IoT devices, this helps extend battery life and reduce device operating costs.
Therefore, 8-bit quantization has become one of the key technologies to solve the “big eater” problem of AI models and promote the inclusive development of AI technology.
The Choice Between Fish and Bear’s Paw: Balancing Accuracy and Efficiency
Of course, no technology is perfect, and 8-bit quantization is no exception. Converting high-precision data to low-precision data inevitably brings some loss of accuracy. In some AI tasks with extremely high precision requirements, this loss may affect the model’s performance. Just like compressing an HD photo into an SD photo, although most details remain, you might find some blurriness when zooming in.
To minimize this loss of accuracy, researchers have developed various technologies:
- Post-Training Quantization (PTQ): Quantization is performed directly after model training is completed. This method is simple and fast but may have some impact on model accuracy.
- Quantization-Aware Training (QAT): Simulating the impact of quantization during the model training process, allowing the model to “adapt” to the low-precision environment in advance. This method usually achieves better accuracy performance but requires retraining the model, which has a higher computational cost.
- Mixed Precision Quantization: Using different precisions for parts of the model with different sensitivities. For example, retaining higher precision (such as 16-bit) for layers with high precision requirements, while performing 8-bit quantization on other parts to find the best balance between performance and accuracy.
The “Starry Sea” of 8-bit Quantization: Applications and Future
8-bit quantization technology has been widely used in fields such as image recognition, voice recognition, and natural language processing. Especially in the field of Large Language Models (LLMs) which has exploded in recent years, 8-bit quantization has played a pivotal role. For example, quantization methods like LLM.int8() enable giant models that were originally difficult to run on consumer-grade hardware to perform inference tasks efficiently with less GPU memory.
Latest progress and application cases confirm this:
Studies have pointed out that AI model quantization technology in 2024 is undergoing a key transition from laboratory to large-scale industrial application. Breakthroughs from INT4 to more extreme low-bit quantization, the maturity of automated quantization toolchains, and the collaborative optimization of specialized hardware and quantization algorithms have become core trends. For example, the 4-bit and 8-bit quantized versions of the Yuan 2.0-M32 large model released by Inspur Information have performance comparable to the 70 billion parameter LLaMA3 open-source large model, but the 4-bit quantized version requires only 23.27GB of memory for inference running, which is about 1/7 of the memory of LLaMA3-70B.
In the future, with the continuous improvement of hardware support for low-precision computing and the continuous optimization of quantization algorithms, we will see not only more widespread 8-bit quantization but even 4-bit quantization (INT4) or even lower-bit quantization technologies becoming mainstream. By then, the deployment of AI models will be more flexible, and operation will be more efficient, opening up broader space for the popularization and innovative application of AI technology.
Conclusion
8-bit quantization is like a bridge connecting high-performance AI models with limited computing resources, making the originally “unattainable” AI technology “within reach.” It not only reduces the deployment and operation costs of AI, improves inference speed and energy efficiency, but is also a key step in promoting AI to mobile terminals and edge devices. Through this clever “slimming technique,” we look forward to AI technology better serving everyone and shining in every corner of the digital world.