Awq vs gptq GPTQ是一种4位量化的训练后量化(PTQ)方法，主要关注GPU推理和性能。 该方法背后的思想是，尝试通过最小化该权重的均方误差将所有权重压缩到4位。 Jun 12, 2024 · GPTQ在LLM量化W4A16方向的地位毋庸置疑，它的出发点很朴素，就是试图最小化权重量化后和量化前的层函数误差，对这个最优化问题进行求解后结果包含二阶的Hessian matrix（海森矩阵），详细数学推理公式见文章HELLO七仔：GPTQ 模型量化，论文见：GPTQ，这里不做详细 Nov 16, 2023 · 这些量化模型包含了很多格式GPTQ、GGUF和AWQ，我们来进行介绍. We start by installing the autoawq library, which is specifically designed for quantizing models using the AWQ method. Some previous papers have compare perplexity of different methods. The latest advancement in this area is EXL2, which offers even better performance. 5-32B-Instruct-GPTQ-Int4. GPTQ is arguably one of the most well-known methods used in practice for quantization to 4-bits. Dynamic Range GPTQ: 가중치를 낮은 정밀도로 변환하고 활성화를 낮은 정밀도로 변환하는 함수를 개발합니다. However, for pure GPU inferencing, GGUF may not be the optimal choice. AWQ, on the other hand, utilizes asymmetric vector quantization, where vectors are quantized Feb 21, 2025 · 文章浏览阅读361次。在过去的一年里，大型语言模型(llm)有了飞速的发展，在本文中，我们将探讨几种(量化)的方式，除此以外，还会介绍分片及不同的保存和压缩策略。 Jul 10, 2024 · 那种量化方法更好：GPTQ vs. Requires training data; Llama 2. 22 22:06 浏览量：47 简介：随着大语言模型（llm）的广泛应用，模型量化成为了提高模型效率和降低资源消耗的关键技术。 Nov 16, 2023 · 这些量化模型包含了很多格式GPTQ、GGUF和AWQ，我们来进行介绍. 9X的加速；同时在笔记本4070-8G GPU上，使用AWQ仍然可以 以33tokens/second 跑Llama-2-13B模型，此时 Dec 3, 2024 · GPTQ tends to overfit on its calibration data. Reduced vram usage. Description: Deployment on multiple GPUs and only garbled text like !!!!! could be generated. RTN is not data dependent, so is maybe more robust in some broader sense. , koboldcpp, ollama, lm studio) exl2, bc it's the fastest given you can fit it in VRAM gptq, bc old habits die hard awq, what? Looks like new type quantization, called AWQ, become widely available, and it raises several questions. Whereas for bits-and-bytes, EETQ and fp8, weights are quantized by TGI on the fly. GGUF) 到目前为止，我们已经探索了分片和量化技术。虽然这些技术对于你的技能来说很有用，但每次加载模型时都需要应用它们似乎有些浪费。 相反，这些模型通常已经被分片和量化，供我们使用。 Aug 22, 2024 · AWQ tends to be faster and more effective in such contexts compared to GPTQ, making it a popular choice for varied hardware environments. Aug 13, 2024 · AWQ（Activation-aware Weight Quantization）量化是一种基于激活值分布(activation distribution)挑选显著权重(salient weight)进行量化的方法，其不依赖于任何反向传播或重建，因此可以很好地保持LLM在不同领域和模式上的泛化能力，而不会过拟合到校准集，属训练后量化(Post-Training Quantization, PTQ)大类。 What do you think would achieve higher inference speed when I offload all layers to the GPU using GGUF or GPU inherent strategies such GPTQ. We can see an example of some research shown in the recent research paper using HQQ quantization: Nov 16, 2023 · 3. Oct 23, 2023 · GPTQ vs GGML. 5-32B-Instruct-GPTQ-Int4 broken with vLLM on multiple GPUs¶ Model: Qwen2. It is a newer quantization method similar to GPTQ. All the code examples presented in this article use Llama 3. GPTQ has been very popular to create models in 4-bit precision that can efficiently run on GPUs. AWQ 对比分析 Jan 3, 2024 · 量子化とキャリブレーション モデルを試したいとき、GPTQやAWQで量子化されたモデルを使う人が多いです。 量子化されたモデルは、モデルの推論に必要なリソースを削減できるほか、（量子化ライブラリによりますが）量子化されていないモデルに比べ推論速度を向上させることができます gptq、gguf、ggml、ptq、qat、awq、aqlm 到底有何不同？ 本文将带你深入探讨这些常见量化技术，助你轻松选择适合自己的模型，快来一探究竟吧！ LLM 量化方法区别 —— GPTQ、GGUF、GGML、PTQ、QAT、AWQ、AQLM 之间的区别是啥？ AutoGPTQ is a framework built on GPTQ, allowing for rapid dequantization and inference/serving of LLMs that have been quantized with GPTQ. Feb 19, 2024 · 文章浏览阅读6k次，点赞24次，收藏53次。本文简要介绍了两种比较常用的大模型量化方法 GPTQ、LLM. Previously, GPTQ served as a GPU-only optimized quantization method. New Nvidia Nemotron-Ultra model support. Feb 17, 2025 · 5. Static Range GPTQ: 가중치 및 활성화를 낮은 정밀도로 변환할 수 있습니다. It uses asymmetric quantization and does so layer by layer such that each layer is processed independently before continuing to the next: Jul 3, 2024 · AWQ量化目前还不支持 Gemma 或 DeciLM 等新架构; 总结. AWQ: An even "smarter" format than GPTQ. New experimental multi-gpu quantization support. The methods include GPTQ, AWQ, EXL2, and others, and the comparison covers perplexity, VRAM, speed, model size, and loading time. updated Sep 26, 2024. We are actively working for the support, so please stay tuned. 预量化 (GPTQ vs. AWQ operates on the premise that not all weights hold the same level of importance, and excluding a small portion of these weights from the quantization process, helps to mitigate the loss of accuracy typically associated with quantization. 4w次，点赞419次，收藏370次。本文介绍了模型量化技术，特别是awq和gptq两种方法。awq通过观察激活而非权重进行低位权重量化，保护少量关键权重；而gptq则针对大规模gpt模型优化，采用一次性量化方法，降低模型大小和计算需求，同时保持高准确度。 awq 量化精度比 gptq 高一点，并且 awq 比 gptq 更容易实现，计算性能更高。 相比 AWQ 采用 heuristic 的方法来寻找 最佳的 scale 和 clip 系数，新的 OminiQuant 则采用训练的方式来获得相应的系数，论文数据比 AWQ 获得更高的量化准确度。 本系列将针对大模型的一些常见训练后量化方案（GPTQ、LLM. Apr 7, 2024 · 这些量化模型包含了很多格式GPTQ、GGUF和AWQ，我们来进行介绍. AWQ is faster than GPTQ. New Dream model support. This means that the weights which contribute the most to the output get the most bits, regardless of where they are in the model. Dec 15, 2024 · As you can see, AWQ can obtain better perplexity than round-to-nearest (RTN) quantization and GPTQ. AWQ vs. Compare the perplexity, VRAM, speed, model size, and loading time of different quantization methods for running llama-2-13b on RTX 3090 GPU. We recommend using the official quantization scripts for creating your quants: AWQ; GPTQ Feb 7, 2025 · 文章浏览阅读1. I know there is a difference between AWQ and GPTQ as well but I would generally like the direction. See the pros and cons of each method, the code examples, and the inference speed and accuracy results. 23 and 0. Nov 26, 2024 · 本文将深入对比三种主流的大语言模型量化方法：GPTQ、GGUF和AWQ，从多个维度剖析它们的异同，为读者提供选择参考。 一、量化方法概述. 除了上面两种以外，一种新格式是AWQ(激活感知权重量化)，它是一种类似于GPTQ的量化方法。AWQ和GPTQ作为方法有几个不同之处，但最重要的是AWQ假设并非所有权重对LLM的性能都同等重要。 Summary. We can conclude from the results that AWQ performs similarly to GPTQ-R while being much faster. In terms of speed , SmoothQuant shows the best performance, followed by AWQ. GPTQ is post training quantization method. Initial support for AWQ (performance not optimized) Support for RoPE scaling and LongChat Support for Mistral-7B Many bug fixes Don't sleep on AWQ if you haven't tried it yet. GGUF, as described, grew out of CPU inference hacks. gptq 的思想最初来源于 yann lecun 在 1990 年提出的 obd 算法，随后 obs 、obc（ obq ） 等方法不断进行改进，而 gptq 是 obq 方法的加速版。gptq 的量化有严谨的数学理论推导，所有的算法步骤都有理论支撑。为了理解 gptq 的思想，我们需要先介绍 obd -> obs -> obq 的演进过程。 Oct 26, 2023 · Artificial Intelligence Market Size, Share & Industry Analysis, By Component (Hardware, Software, Services), By Deployment (On-premise & Cloud), By Enterprise Type (Large Enterprises, Small & Medium-sized Enterprises), By Function (Human Resources, Marketing & Sales, Product/Service Deployment, Service Operation, Risk, Supply-chain Management), By Technology (Machine Learning, Natural Language Better performance for GPTQ & AWQ We extend the marlin kernel to desc-act GPTQ model as well as AWQ model with zero points, and repack the model on the fly. It is widely adapted to almost all kinds of model and can be run on may engines. However, it has been surpassed by AWQ, which is approximately twice as fast. 除了上面两种以外，一种新格式是AWQ(激活感知权重量化)，它是一种类似于GPTQ的量化方法。AWQ和GPTQ作为方法有几个不同之处，但最重要的是AWQ假设并非所有权重对LLM的性能都同等重要。 May 23, 2024 · 文章浏览阅读7. AWQ and GGUF can be combined in this PR, the method can leverage useful information from AWQ to scale weights. Feb 19, 2024 · GPTQ. This is a big problem with AWQ and GPTQ, and means that many people are training in bnb nf4, then merging to an GPTQ and AWQ models can fall apart and give total bullshit at 3 bits while the same model in q2_k / q3_ks with around 3 bits usually outputs sentences. A direct comparison between llama. Sep 19, 2024 · Understanding GPTQ, AWQ, and GGUF GPTQ. Requires training data; AWQ - "Activation-aware Weight Quantization". This article provides an overview of various quantization techniques applied to Large Language Models (LLMs) for model compression, including GPTQ, GGML, QAT, AWQ, and their practical implementation through code examples and explanations of the quantization process. Apr 5, 2024 · GPTQ改进点在于 量化的效率更高，OBQ分层迭代量化，而GPTQ是一次量化（one-step）；作者发现GPTQ算法采用同时按照相同的顺序进行量化。而不是每个权重tensor用贪心策略（耗时太长）； 步骤; 初始化量化结果Q，误差矩阵E；分解输入的Hessian矩阵，得到H-1的逆矩阵 Seeing as I found EXL2 to be really fantastic (13b 6-bit or even 8-bit at blazing fast speeds on a 3090 with Exllama2) I wonder if AWQ is better, or just easier to quantize. cpp, AutoGPTQ, ExLlama, and transformers perplexities A direct comparison between llama. int8()、SmoothQuant、AWQ等）进行讲述。 大模型量化概述 大模型量化技术原理-GPTQ、LLM. New Phi4-MultiModal model support . We will see how fast they are for fine-tuning and their performance with QLoRA. 什么是分组精度调整 Apr 24, 2025 · 与gptq相比，awq可以显著提高推理速度，同时保持类似甚至更好的性能。 Models的缩写，即GPT模型的后训练 量化 。 ，那么其专注于GPU的优化可能会成为一个劣势。 Feb 1, 2024 · 原文：大语言模型量化方法对比：GPTQ、GGUF、AWQ - 知乎 在过去的一年里，大型语言模型(llm)有了飞速的发展，在本文中，我们将探讨几种(量化)的方式，除此以外，还会介绍分片及不同的保存和压缩策略。 Mar 5, 2025 · AWQ\GPTQ量化模型运行方式（测试下来感觉GPU都会占满，4090卡不量化运行90 tokens/s，AWQ\GPTQ 版30左右 tokens/s）如果是用OPENAI包 model还是写 名称填的–lora-modules qwen-lora；不填这个默认vllm模型不会加载使用lora。如果是这个名称填的–lora-modules qwen-lora。 Jul 15, 2024 · GPTQ - One of the older quantization methods. AWQ量化. 激活感知权重量化(awq)，一种新格式是awq(激活感知权重量化)，它是一种类似于gptq的量化方法。 一种面向LLM低比特权重量化的硬件友好方法。 我们的方法基于这样一个观察:权重并非同等重要，仅保护1%的显著权重可以大大减少量化误差。 Qwen2. After that, you can use the quantization techniques from llama. Mar 18, 2024 · This article discusses various techniques to quantize models like GPTQ, AWQ and Bitsandbytes. cpp, AutoGPTQ, ExLlama, and transformers perplexities Table of contents Nov 16, 2023 · 文章浏览阅读6. GPTQ是一种4位量化的训练后量化(PTQ)方法，主要关注GPU推理和性能。 该方法背后的思想是，尝试通过最小化该权重的均方误差将所有权重压缩到4位。 Feb 15, 2024 · AWQ gives better accuracy than GPTQ, while SmoothQuant falls behind them. Tanuki-8BのAWQによる量子化は特にライブラリの改変等なしでそのまま変換できます。 まず、AWQ量子化のためのライブラリであるAutoAWQを通常通りインストールします。 Oct 14, 2023 · AWQ\GPTQ量化模型运行方式（测试下来感觉GPU都会占满，4090卡不量化运行90 tokens/s，AWQ\GPTQ 版30左右 tokens/s）如果是用OPENAI包 model还是写 名称填的–lora-modules qwen-lora；不填这个默认vllm模型不会加载使用lora。如果是这个名称填的–lora-modules qwen-lora。 Dec 25, 2024 · 那种量化方法更好：GPTQ vs. 简介：本文深入探讨开源模型在实际应用中的落地挑战，聚焦于模型量化技术，特别是awq与gptq两种量化方法的对比。通过简明扼要的语言和实例，帮助读者理解复杂 Sep 17, 2024 · Our findings reveal that (1) quantized models generally surpass smaller FP16 baselines, yet they often struggle with instruction-following and hallucination detection; (2) FP8 consistently emerges as the most robust option across tasks, and AWQ tends to outperform GPTQ in weight-only quantization; (3) smaller models can suffer severe accuracy There are several weight-only quantization methods for LLMs, with AWQ and GPTQ among the most prominent. GPTQ是一种4位量化的训练后量化(PTQ)方法，主要关注GPU推理和性能。 该方法背后的思想是，尝试通过最小化该权重的均方误差将所有权重压缩到4位。 23 votes, 12 comments. GPTQ是 Post-Training Quantization for GPT Models的缩写，即GPT模型的后训练量化. Mar 9, 2025 · AWQ\GPTQ量化模型运行方式（测试下来感觉GPU都会占满，4090卡不量化运行90 tokens/s，AWQ\GPTQ 版30左右 tokens/s）如果是用OPENAI包 model还是写 名称填的–lora-modules qwen-lora；不填这个默认vllm模型不会加载使用lora。如果是这个名称填的–lora-modules qwen-lora。 Nov 19, 2023 · 这些量化模型包含了很多格式GPTQ、GGUF和AWQ，我们来进行介绍. Jan 16, 2024 · Compare and contrast three quantization methods for optimizing performance and resource efficiency of LLMs: GGUF, GPTQ, and AWQ. 7B-Q4_K_M 和 13B_Q2_K，哪个更好？ 性能表现如何？ GGUF 文件结构; 直观感受 GGUF It’s slower (-25% to -50% speed) but if we use GPTQ without reordering the performance of the model degrades to a point where it may become worse than the much more naive RTN quantization. It does this by dissecting the information question to track down the catchphrases, expressions, and hints about the setting that are expected to produce an exact response. The results comparison of quantization for Llama adapted by the paper [2] Note that AWQ is sometimes inferior to GPTQ for some models, such as the Mistral models and instruction-tuned models, according to the paper. QuIP# performs better than all other methods at 2-bit precision, but creating a QuIP# quantized model is very expensive. Is it supports old (Pascal) GPUs with fp32 or only for new one with fp16 calculations? Last time I try to load GPTQ via AutoGTPQ in Oobabooga on my p40 - it works slower than GGUF. 5-bit quantization where 24GB would run a 70b model? Mar 16, 2025 · awq 实现了出色的量化性能，尤其是对于指令调整的 lm 和多模态 lm。即使对于具有大量参数的模型（例如，gpt 模型中的 1750 亿个参数），gptq 也可以实现高精度和高效的量化。 It ultilizes a calibration dataset to improve quality at the same bitrate. The preliminary result is that EXL2 4. OWQ, SpQR, and SqueezeLLM require more complex computation for their quantization schemes, hence slower. 08. Takes a lot time and vram+ram to make a GPTQ quant. 相同位数下量化方法的区别？ 2. Assuming that the quantization is the same. From the AWQ paper: Apr 17, 2025 · SmoothQuant、GPTQ、AWQ 全家桶横评：精度 vs 吞吐 vs 兼容性实测报告 摘要： 你是否遇到过这样的选择困难：训练后的大模型想要部署，却不知道到底该用 GPTQ、AWQ 还是 SmoothQuant？ 有的快但精度掉、有的稳但不好接框架、有的部署麻烦但推理极致。 Dec 11, 2023 · AWQ seems best on this front, then GPTQ, then bitsandbytes-nf4. Consequently, we adopt GPTQ as our primary method for weight quantization. So AWQ does deprecate GPTQ in accuracy. 125b seems to outperform GPTQ-4bit-128g while using less VRAM in both cases. Typically, these quantization methods are implemented using 4 bits. To leverage GPTQ, AWQ, Marlin and EXL2 quants, you must provide pre-quantized weights. GGUF - Sharding the model into smaller pieces to reduce memory usage. Since many weight-only quantization schemes reduce weight bit-width to INT4 or even lower, dedicated computation kernels are necessary to accelerate its computation. In theory it delivers better quality than GPTQ of the same bitrate. 1、GPTQ: Post-Training Quantization for GPT Models. AWQ is better than GPTQ (especially with act order false) bnb nf4 quality seems better than AWQ and GPTQ; Ability to merge LoRA adapters to the base model. 7-3. 35, respectively. Nov 13, 2023 · A new format on the block is AWQ (Activation-aware Weight Quantization) which is a quantization method similar to GPTQ. How fast are token generations against GPTQ with Exllama (Exllama2)? Does this new quantization require less VRAM than GPTQ? Is it possible to run 70B model on 24GB GPU ? How good it at keeping context? A detailed comparison between GPTQ, AWQ, EXL2, q4_K_M, q4_K_S, and load_in_4bit: perplexity, VRAM, speed, model size, and loading time. 0: 🎉 New ground-breaking GPTQ v2 quantization option for improved model quantization accuracy validated by GSM8K_PLATINUM benchmarks vs original gptq. Quantization techniques that aren’t supported in Transformers can be added with the HfQuantizer class. Apr 18, 2025 · INT4 / INT8 横评：GPTQ、AWQ、SmoothQuant 谁才是推理最优解？** 个人简介 作者简介：全栈研发，具备端到端系统落地能力，专注大模型的压缩部署、多模态理解与 Agent 架构设计。 在这篇博客中，我们将学习流行的量化技术，如GPTQ、AWQ和Bitsandbytes(QLoRA)。GPTQ和AWQ被归类为PTQ，而QLoRA被归类为QAT。这些算法已经集成到transformers库中，因此用户可以高效地使用它们。在接下来的章节中，我们将简要检查这些算法的理论。 2. Sep 9, 2024 · AWQ量子化 Tanuki-8Bの変換. 本文讨论了使用 GPTQ、AWQ 和 Bitsandbytes 等各种技术对模型进行量化。它探讨了每种方法的利弊(GPTQ vs AWQ vs Bitsandbytes)，解释了使用这些方法对 Hugging Face 模型权重进行量化的过程，最后使用量化权重进行 LLM 推理。 Feb 23, 2025 · AWQ\GPTQ量化模型运行方式（测试下来感觉GPU都会占满，4090卡不量化运行90 tokens/s，AWQ\GPTQ 版30左右 tokens/s）如果是用OPENAI包 model还是写 名称填的–lora-modules qwen-lora；不填这个默认vllm模型不会加载使用lora。如果是这个名称填的–lora-modules qwen-lora。 GPTQ is quite data dependent because it uses a dataset to do the corrections. 4w次，点赞419次，收藏370次。本文介绍了模型量化技术，特别是awq和gptq两种方法。awq通过观察激活而非权重进行低位权重量化，保护少量关键权重；而gptq则针对大规模gpt模型优化，采用一次性量化方法，降低模型大小和计算需求，同时保持高准确度。 Apr 22, 2024 · We explore a range of cutting-edge quantization methods across technical tracks (RTN, GPTQ , AWQ , SmoothQuant , PB-LLM , QuIP , DB-LLM , and BiLLM for PTQ; QLoRA and IR-QLoRA for LoRA-FT), covering a wide spectrum from 1 to 8 bits and utilizing a diverse array of evaluation datasets, including WikiText2, C4, PTB, CommonSenseQA datasets (PIQA AWQ, Activation-aware Weight Quantization SmoothQuant的续作, 从源代码来看, 它对SmoothQuant中计算scale时需要的超参alpha, 增加 了一步通过grid search得到每个scale的最优参数, 但论文的故事包装得很好, 同时取得的效果也是十分显著的, 符合大道至简的准则. Aug 7, 2023 · Why AWQ is faster than GPTQ. Learn how to implement and use them on E2E Cloud GPU platform with examples and code. Regarding your question, this is my understanding: While the performance highly depends on the kernel implementation, AWQ is meant to be (slightly) faster than GPTQ, when both are equally optimized. int8() May 21, 2024 · 文章浏览阅读2. There are several differences between AWQ and GPTQ as methods but the most important one is that AWQ assumes that not all weights are equally important for an LLM’s performance. 4 KB Apr 29, 2024 · GPTQ는 GPU에서 선호되며 CPU에서는 사용되지 않습니다. Feb 25, 2025 · GPTQ（Generalized Post-Training Quantization ） 定义： GPTQ 是一种后训练量化技术，专门为大规模语言模型设计。它通过对模型权重进行逐层优化，实现高效的量化。 核心思想： GPTQ 在模型训练完成后，对每一层的权重进行量化。 Nov 12, 2023 · Hugging FaceのtransformersにAutoAWQが統合されていたので、VRAM12GBのGPU(RTX3060)で、LLM(ELYZA-japanese-Llama-2-7b-fast-instruct)のAWQ量子化を試してみました。 It's hard to make an apples-to-apples comparison of the different quantization methods (GPTQ, GGUF, AWQ and exl2), but in theory being smart about where you allocate your precious bits should improve the model's precision. 9 and 0. !pip install vllm Feb 4, 2025 · 与GPTQ相比，AWQ可以显著提高推理速度，同时保持类似甚至更好的性能。Models的缩写，即GPT模型的后训练量化。 Models的缩写，即GPT模型的后训练量化。 ，那么其专注于GPU的优化可能会成为一个劣势。 So GPTQ, exl2 and AWQ all have this "activation order" based quantization option. Following the latency for 256 input size and 256 output size with Mistral-7B quants. Framework: vLLM. GPTQ/AWQ is tailored for GPU inferencing, claiming to be 5x faster than GGUF when running purely on GPU. It looks at the pros and cons of each method (GPTQ vs AWQ vs bitsandbytes), explains quantizing hugging-face model weights using these methods and finally use quantize weights for LLM inference. AWQ - Quantizing the model weights and activations to Apr 16, 2025 · 推理速度: 使用 fuse_layers=True 加载时，AWQ 的推理速度通常非常快，可能优于未优化的 GPTQ kernel。 精度: AWQ 在 4bit 下通常能保持非常好的性能，尤其是在保护关键权重方面表现突出。 四、GPTQ vs. Is it faster than EXL2? Does it have usable ~2. Workaround: Each of the following workaround could be considered. Functionality of GPTQ. 15 03:20 浏览量：6. A user shares a detailed comparison of different quantization methods for Llama, a large language model created by Meta AI. And u/kpodkanowicz gave an explanation why EXL2 could have been so bad in my tests: 04/13/2025 3. cpp to quantize the scaled awq model like normal. The advantage of AWQ, on a scale of 0–100, is only 0. 아래는 GPTQ의 다양한 유형입니다. This means once you have your pre trained LLM, you simply convert the model parameters into lower precision. 7. 2. AWQ GPTQ GPTQ是Post-Training Quantization for GPT Models的缩写，即GPT模型的后训练量化 GPTQ是一种针对4位量化的后训练量化方法，主要侧重于在GPU上提升推理性能。 该方法的核心思想是通过将所有权重压缩到4位量化，通过最小化权重的均方误差来实现量化。在推理过程中，它 Mar 9, 2024 · We would like to show you a description here but the site won’t allow us. 8, respectively. Notably, this optimization is Mar 8, 2025 · AWQ\GPTQ量化模型运行方式（测试下来感觉GPU都会占满，4090卡不量化运行90 tokens/s，AWQ\GPTQ 版30左右 tokens/s）如果是用OPENAI包 model还是写 名称填的–lora-modules qwen-lora；不填这个默认vllm模型不会加载使用lora。如果是这个名称填的–lora-modules qwen-lora。 AWQ SpQR GPTQ. TheBloke has already quantized your favorite model and output quality is significantly better then any GPTQ at 4bits. GGUF vs. Optimised Quants for high-throughput deployments! Compatible with Transformers, TGI & VLLM 🤗 Jan 19, 2024 · AWQ vs GPTQ share your experience !!! (win10, RTX4060-16GB) LOADING AWQ 13B dont work VRAM overload (GPU-Z showes my limit 16GB) The 13B GPTQ file only uses 13GB and works well next: Test on 7B GPTQ(6GB VRAM) Dec 26, 2023 · 文章浏览阅读1w次，点赞26次，收藏42次。本文介绍了模型量化技术，包括其原理、常见方法如Roundnearestquantization、AWQ、GPTQ和BitsAndBytes，以及在减小模型大小、加速推理、降低功耗和提高资源受限设备可用性等方面的应用。 AWQ vs GPTQ：量化方法对比 GPTQ（Generalized Post-Training Quantization） 特点： 后训练量化：GPTQ在模型训练完成后进行量化，无需重新训练模型。 灵活性：支持多种量化精度（如int8、int4等），并可根据具体需求进行配置。 AWQ has lower perplexity and better generalization than GPTQ. AWQ（Activation Weight Quantization）是另一种训练后量化方法，与GPTQ类似，但针对非GPU设置（如笔记本电脑或Mac）进行了优化，以获得更好的性能。在使用激活重新排序时，AWQ特别有用，即使量化数据与推理数据集不同，它也能提高准确性。. 03. GPTQ and GGML are currently the two primary methods for model quantization, but what are the differences between them? And which quantization method should you choose? quantizations Thank you for the info! :3 I'm learning about these analytical techniques for the first time and this exercise has been a very helpful introduction to the theory of perplexity testing. 在选择gptq还是awq时，需要根据具体的优先级和约束条件进行权衡 6。 选择awq的情况： 精度是首要考虑因素： 研究表明，awq通常比gptq能更好地保持精度，尤其是在处理复杂的模型和任务时 12。 GPTQ (full model on GPU) GGUF (potentially offload layers on the CPU) GPTQ. int8() 属于 round-to-nearest (RTN) 量化：舍入到最近的定点数。 My guess for the end result of the poll will be gguf >> exl2 >> gptq >> awq. Jul 31, 2023 · GPTQ was used with the BLOOM (176B parameters) and OPT (175B parameters) model families, and models were quantized using a single NVIDIA A100 GPU. Nov 16, 2023 · 这些量化模型包含了很多格式GPTQ、GGUF和AWQ，我们来进行介绍. AWQ, Activation-aware Weight Quantization SmoothQuant的续作, 从源代码来看, 它对SmoothQuant中计算scale时需要的超参alpha, 增加 了一步通过grid search得到每个scale的最优参数, 但论文的故事包装得很好, 同时取得的效果也是十分显著的, 符合大道至简的准则. GPTQ是一种4位量化的训练后量化(PTQ)方法，主要关注GPU推理和性能。 该方法背后的思想是，尝试通过最小化该权重的均方误差将所有权重压缩到4位。 May 31, 2024 · Tests How does quantisation affect model output? - 15 basic tests on different quant levels A detailed comparison between GPTQ, AWQ, EXL2, q4_K_M, q4_K_S, and load_in_4bit: perplexity, VRAM, speed, model size, and loading time. HQQ offers competitive quantization accuracy while being very fast and cheap to quantize and not relying on a calibration Oct 4, 2024 · In this article, we will experiment and compare HQQ, AQLM, AutoRound, bitsandbytes, and GPTQ for QLoRA fine-tuning. GPTQ（Post-Training Quantization for GPT Models）： GPTQ是一种针对GPT模型的后训练量化方法，主要侧重于在GPU上提升推理性能。 Feb 17, 2025 · 5. As part of the AutoGPTQ stack, they provide a Triton GPTQ kernel to handle the dequantization of a model for inference. awqは先月新しく公開された、gptqよりも高性能な量子化アルゴリズムです。 特徴としては、量子化する時に重みの重要性を判断する機能があって、重要ではないと判断された重みはスキップ（削除）して、重要な重みだけ量子化することができました。 Nov 29, 2023 · Hi @frankxyy, vLLM does not support GPTQ at the moment. Usually comes at 3, 4, or 8 bits. int8()；LLM. ) awq 量化精度比 gptq 高一点，并且 awq 比 gptq 更容易实现，计算性能更高。 相比 AWQ 采用 heuristic 的方法来寻找 最佳的 scale 和 clip 系数，新的 OminiQuant 则采用训练的方式来获得相应的系数，论文数据比 AWQ 获得更高的量化准确度。 本系列将针对大模型的一些常见训练后量化方案（GPTQ、LLM. 1k次，点赞4次，收藏6次。格式主要特点适用场景PyTorch灵活、易调试、存储完整模型信息训练、微调、推理AWQ量化优化、高效推理、保留高精度低比特量化、边缘计算、大模型推理GPTQ高效量化、低开销、高推理效率服务器端推理、云端部署、大规模 Transformer不同的模型格式适用于不同 Aug 22, 2024 · GGML vs GPTQ. Llama 3. 1 but it would work the same for other LLMs supported by these quantization methods. 3. AWQ¶. Loading, dequantization, and execution of post-dequantized weights are highly parallelized, offering a substantial inference improvement versus the original CUDA GPTQ kernel. Oct 24, 2024 · GPTQ vs AWQ: Generalized Product Quantization (GPTQ) and Asymmetric Quantization Vectors (AWQ) are two popular quantization techniques used in deep learning. Because of this, when choosing between AWQ and GPTQ models, AWQ should always be the better choice. GPTQ. GPTQ是一种针对 4位量化 的 后训练量化 方法，主要侧重于 在 GPU上提升推理性能 。 Jan 20, 2025 · 由于 Machete 不支持 AWQ，因此我们在 GPTQ 下对比了 vLLM 的不同内核实现。 图 6. It is not faster than exllama because exllama runs a lot of kernel optimizations on top to make it faster. This is meant for GPU or CPU Analyze the trade-offs, performance, and applicability of GPTQ, AWQ, and SmoothQuant. The example model was already sharded. Mar 13, 2024 · AWQ 在多种语言建模任务和领域特定基准测试中表现出色，包括指令调整的语言模型和多模态语言模型。这种方法支持在内存和计算能力有限的边缘设备（如 NVIDIA Jetson Orin 64GB）上部署大型模型，如 Llama-2-70B 模型。 AWQ vs GPTQ Transformers supports the AWQ and GPTQ quantization algorithms and it supports 8-bit and 4-bit quantization with bitsandbytes. If you are aiming for pure efficient GPU inferencing, two names stand out - GPTQ/AWQ and EXL2. gptq与awq的适用场景. Perplexity. However, we found that, on real-world benchmarks, GPTQ consistently outperforms AWQ by larger margins of 2. AWQ is more hardware efficient and simpler to implement than SpQR, but the compression ratio seems to be worse than SpQR. 1. Practical Example. Sep 30, 2024 · 3、AWQ: Activation-aware Weight Quantization. 1 GPTQ, AWQ, and BNB Quants. Mar 12, 2025 · AWQ\GPTQ量化模型运行方式（测试下来感觉GPU都会占满，4090卡不量化运行90 tokens/s，AWQ\GPTQ 版30左右 tokens/s）如果是用OPENAI包 model还是写 名称填的–lora-modules qwen-lora；不填这个默认vllm模型不会加载使用lora。如果是这个名称填的–lora-modules qwen-lora。 Mar 18, 2024 · This article discusses various techniques to quantize models like GPTQ, AWQ and Bitsandbytes. In my opinion, comparing AWQ with GPTQ-R is fair and relevant. true. Sep 1, 2023 · なお、独自のデータセットを文字列のリストとして渡すこともできます。しかし、gptq論文のデータセットを使うことを強く推奨します。 gptqにはキャリブレーションセットなるデータセットが必要になる。例えば Aug 14, 2024 · 开源模型应用落地：模型量化的深度剖析与awq vs gptq实战对比 作者：kakaka 2024. Some posts allege it's faster than GPTQ, but EXL2 is also faster than GPTQ. Source AWQ. Question Getting it: GPTQ can comprehend the importance and setting of issues that are presented to it. Marlin is a 4-bit only CUDA GPTQ kernel, highly optimized for the NVIDIA A100 GPU (Ampere) architecture. 4b seems to outperform GPTQ-4bit-32g while EXL2 4. GPTQ是一种4位量化的训练后量化(PTQ)方法，主要关注GPU推理和性能。 该方法背后的思想是，尝试通过最小化该权重的均方误差将所有权重压缩到4位。 We would like to show you a description here but the site won’t allow us. 不过还好，做GPTQ-for-LLaMA那个老哥自己又测了一次，结果如下: 根据他的结果，他的判断是. Mar 22, 2024 · 大语言模型量化方法深度解析：gptq、gguf与awq的对比 作者： da吃一鲸886 2024. Like in the automotive industry where the official fuel consumption values are often not achievable in real life, the same can be true for LLM benchmarks. Using a single GPU Nov 4, 2023 · This novel development allows users to effectively apply GPTQ quantization, enabling the quantization of preferred language models to 8, 4, 3, or even 2 bits. But the problem is that exllama is written explicitly to optimize LLaMa models, so the full performance boost will not be seen in other models. I created all these EXL2 quants to compare them to GPTQ and AWQ. Mar 18, 2024 · Learn how to quantize large language models (LLMs) with different techniques, such as GPTQ, AWQ, and Bitsandbytes. TGI supports GPTQ, AWQ, bits-and-bytes, EETQ, Marlin, EXL2 and fp8 quantization. AWQ. gguf, bc you can run anything, even on a potato EDIT: and bc all the most popular frameworks use it only (eg. Oct 8, 2024 · RTN vs GPTQ vs AWQ vs GGUF（GGML） 速览; 什么是 PPL？ 理解 GGUF 模型文件名; 新的 GGML 量化方法：k-quants; 为什么需要新的量化方法？ 新的量化方法有哪些？ QA; 1. AWQ, on the other hand, utilizes asymmetric vector quantization, where vectors are quantized Feb 21, 2025 · 文章浏览阅读361次。在过去的一年里，大型语言模型(llm)有了飞速的发展，在本文中，我们将探讨几种(量化)的方式，除此以外，还会介绍分片及不同的保存和压缩策略。 Oct 24, 2024 · GPTQ vs AWQ: Generalized Product Quantization (GPTQ) and Asymmetric Quantization Vectors (AWQ) are two popular quantization techniques used in deep learning. May 9, 2024 · 3、 AWQ: Activation-aware Weight Quantization. 4k次，点赞8次，收藏8次。awq(激活感知权重量化)，它是一种类似于gptq的量化方法。所以他们的论文提到了与gptq相比的可以由显著加速，同时保持了相似的，有时甚至更好的性能。 AWQ基本都能胜出；对于多模态模型 VLM（visual language model），上述实验配置的GPTQ效果不佳；此外，基于group的量化 看起来是比较有效的； 加速效果 相比huggingface FP16，有2. See the results for GPTQ, AWQ, EXL2, q4_K_M, q4_K_S, and load_in_4bit models. EDIT: To answer your question as well, I believe they measure it differently than you do. AWQ（Activation Weight Quantization）是另一种训练后量化方法，与GPTQ类似，但针对非GPU设置（如笔记本电脑或Mac）进行了优化，以获得更好的性能。在使用激活重新排序时，AWQ特别有用，即使量化数据与推理数据集不同，它也能提高准确性。 Apr 7, 2024 · 预量化(gptq、awq、gguf) 我们已经探索了分片和量化技术。 但是量化是在每次加载模型时进行的，这是非常耗时的操作，有没有办法直接保存量化后的模型，并且在使用时直接加载呢？ Aug 11, 2023 · This is the fastest that you can run AWQ models right now. They most likely measured GPTQ Triton matrix-vector speed vs the AWQ matrix-matrix speed as mentioned in the paper: The document discusses and compares three different quantization methods for loading large language models (LLMs): 1. AWQ is data dependent because data is needed to choose the best scaling based on activation (remember activations require W and v (the inputs)). Using the AWQ or GPTQ-Int8 variants. Installing AutoAWQ Library. AWQ（Activation-Aware Layer Quantization）是一种静态的后训练量化技术。其思想基于：有很小一部分的权重十分重要，为了保持性能这些权重不会被量化。 AWQ 的优势在于其需要的校准数据集更小，且在指令微调和多模态模型上表现良好。 Apr 17, 2025 · SmoothQuant、GPTQ、AWQ 全家桶横评：精度 vs 吞吐 vs 兼容性实测报告 摘要： 你是否遇到过这样的选择困难：训练后的大模型想要部署，却不知道到底该用 GPTQ、AWQ 还是 SmoothQuant？ 有的快但精度掉、有的稳但不好接框架、有的部署麻烦但推理极致。 Dec 11, 2023 · AWQ seems best on this front, then GPTQ, then bitsandbytes-nf4. Apr 24, 2024 · 4. 1. The results can be found more at here: AutoAWQ GPTQ/AWQ - Made for GPU inferencing, 5x faster than GGUF when running purely on GPU. 在小批量设置下，TensorRT-LLM 最优配置与 vLLM 各内核选项的吞吐量对比 1400×718 88. Looks like new type quantization, called AWQ, become widely available, and it raises several questions. GPTQ - HuggingFace's standard method without quantization which loads the full model and is least efficient. GPTQ employs a product quantization approach, where vectors are quantized into multiple subvectors and each subvector uses its own codebook. 3k次，点赞3次，收藏9次。本文讨论了如何通过量化、分片和不同的保存和压缩技术优化大型语言模型（LLMs）的加载和性能，包括GPTQ、GGUF和AWQ等方法，以及如何在HuggingFaceTransformers库中实现这些技术。 Marlin. 💻 Quantize an LLM with AutoGPTQ. 0. uukag kdclj rzkk edxe xty zanb nezdb sflaqbsf byts iuxqz