Awq vllm t… If unspecified, will use the default value of 0. 5, we release a number of base language models and instruction-tuned language models ranging from 0. Model Quantization (INT8 W8A8, AWQ, GPTQ) Chunked-prefill. nlp Safetensors PyTorch deepseek_v3 License: mit English Chinese @ 37,172 downloads. 5-3B-Instruct-AWQ 模型进行少样本学习，包括模型的加载、数据的准备、推理过程的优化，以及结果的提取和评估。 关键步骤是： 使用 vLLM（为了提高速度） 使用 AWQ 4 位量化（以避免 GPU VRAM OOM） Qwen2. 此外，如果 AWQ 无法满足精度需求，我们建议使用 章节介绍的 AWQ+OmniQuant 组合算法 来进一步提升精度。 Mar 6, 2025 · Although vLLM supports this model, this model supports both reasoning and tool calling, and currently vLLM does not support using --enable-reasoning and --enable-auto-tool-choice together. Mar 13, 2024 · I convert model follow AutoAWQ library as follow script. Linear method for AWQ. 2. Related runtime environment variables# VLLM_CPU_KVCACHE_SPACE: specify the KV Cache size (e. vLLM supports quantizing weights and activations to INT8 for memory savings and inference acceleration. Possible choices: aqlm, awq, deepspeedfp, fp8, marlin, gptq_marlin_24, gptq_marlin, gptq, squeezellm, compressed-tensors, bitsandbytes, None Method used to quantize the weights. Apr 29, 2025 · vLLM v0. Reload to refresh your session. Note. Fast model execution with CUDA/HIP graph. 5-VL- We would recommend using the unquantized version of the model for better accuracy and higher throughput. 11. 10。 The following 4 ENVs are used to control the profiler: - VLLM_ENGINE_PROFILER_ENABLED, set to true to enable device profiler. “float16” is the same as “half”. cpp等框架, vllm是一个可以产品化部署的方案，适用于需要大规模部署和高并发推理的场景，采用 PagedAttention 技术，能够有效减少… Aug 31, 2024 · You signed in with another tab or window. 0で採用され、TheBloke兄貴もこのフォーマットでのモデルをアップされています。. You signed out in another tab or window. This is a per-instance limit, and only applies to the current vLLM instance. Feb 21, 2025 · PyTorch version: 2. Oct 13, 2023 · @gesanqiu while the README says it works, that's sadly not the case for GPTQ, AWQ, or SmoothQuant, see: NVIDIA/TensorRT-LLM#200. 9. 一般FP6的模型，V100 可以使用，但是AWQ好像就不行了。 Feb 26, 2025 · If you encounter any issues or need advanced usage with lmdeploy, we recommend reading the lmdeploy documentation. 6. vLLM’s torch. quantization. 2 软件环境 操作系统: Linux（验证环境发行版 Ubuntu 22. vLLM supports AWQ, GPTQ and SqueezeLLM quantized models: To use AWQ model you need to install the autoawq library pip install autoawq. - wejoncy/QLLM The saved model is not compatible with vLLM Apr 6, 2025 · 文章浏览阅读898次，点赞12次，收藏20次。本测试使用 QwQ 的两个模型，分别是 4bit 量化版本和 AWQ 版本。AWQ（Activation-aware Weight Quantization）是一种激活感知的权重量化技术，主要用于优化大语言模型（LLM）的推理效率。 Feb 21, 2025 · 如果vLLM不支持GGUF，可能需要建议替代方案，比如使用llama. To create a new 4-bit quantized model, you can leverage AutoAWQ. 34 Python version: 3. 19: The instruction-tuned Qwen2-VL-72B model and its quantized version [AWQ, We recommend using vLLM for fast Qwen2. 5 is the latest series of Qwen large language models. 2 We would recommend using the unquantized version of the model for better accuracy and higher throughput. We recommend using the latest version of vLLM (vllm>=0. - VLLM_ENGINE_PROFILER_REPEAT, number of cycles for (warmup + profile). 2版本无法运行DeepSeek-R1-Distill-Llama-70B模型，vllm v0. Implementing a Basic Model; Registering a vLLM is a fast and easy-to-use library for LLM inference and serving. 1 硬件配置 GPU: 8× NVIDIA A100 80GB (PCIe) 显存要求: 每卡80GB，8卡总显存640GB 系统内存: ≥32GB (用于交换空间) 1. 3、AWQ: Activation-aware Weight Quantization. Introduction We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. Quantize with Marlin from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_path = 'mistralai/Mistral-7B-Instruct-v0. vLLM 的插件系统; vLLM 分页注意力; 多模态数据处理; 自动前缀缓存; Python 多进程处理; V1 设计文档. Mar 16, 2025 · 本文要解析的就是这里的 vllm ops. As of now, it is more suitable for low latency inference with small number of concurrent requests. 5 - VL - 72 B而言，应用 AWQ 可以大幅削减存储需求以及跨设备传输所需带宽，特别适合边缘端 部署 场景下的轻量化改造 A high-throughput and memory-efficient inference and serving engine for LLMs - vllm-project/vllm Welcome to vLLM#. py:440] awq quantization is not fully optimized yet. Contributing to vLLM; Profiling vLLM; Dockerfile; Adding a New Model. Especially Deepseek-R1 is a A general 2-8 bits quantization toolbox with GPTQ/AWQ/HQQ/VPTQ, and export to onnx/onnx-runtime easily. AWQ models are also supported directly through the LLM entrypoint: from vllm import LLM, SamplingParams # Sample prompts. 安装依赖库. Memory Usage Testing#. json is set to 40,960, which used by vLLM, if --max-model-len is not specified. 5. 6k次，点赞25次，收藏23次。注：一般来说第一次启动模型会存在各种各样的问题导致模型不能正常启动，遇到的问题百度一下应该能解决。这里下载的Deepseek-R1-70B-AWQ，vLLM不支持4bit的量化版本。已经安装好的跳过，python>=3. The speed can be slower than non-quantized models. 2 days ago · 借助vLLM，构建一个与OpenAI API兼容的API服务十分简便，该服务可以作为实现OpenAI API协议的服务器进行部署。适用于大批量Prompt输入，并对推理速度要求高的场景，吞吐量比HuggingFace Transformers高10多倍。 AWQ. benchmark最高并发请求：60+参数：两个4090，… Jan 29, 2025 · Ollama employs GGUF compression, while vLLM supports both GGUF and AWQ methods. Quantization: GPTQ, AWQ, INT4, INT8, and FP8 We would recommend using the unquantized version of the model for better accuracy and higher throughput. It seems that both of them use around the same amount of GPU memory, whereas I'd expect the AWQ quantized version to use far less memory. However, when using AWQ, it looks V100 (GPU level 70) cannot handle (at least 75). This parameter should be set Dec 17, 2024 · Defaulting to 'generate'. As a result, vllm cannot load the MTP weights and defaults to using the initialized weights instead, which causes the MTP block’s attention computation to produce all NaNs. 实现基本模型; 向 vLLM 注册模型; 编写单元测试; 多模态 # For FP8 quantized model vllm serve Qwen3/Qwen3-8B-FP8 # For AWQ quantized model vllm serve Qwen3/Qwen3-8B-AWQ 备注 FP8 计算在计算能力 > 8. speculative decoding, like Medusa Attention, see [Discussion] Will vLLM consider using Speculative Sampling to accelerating LLM decoding? Jan 13, 2025 · 2. post1 版本可以运行DeepSeek-R1-Distill-Llama-70B模型。 Apr 15, 2025 · vLLM also incorporates many modern LLM acceleration and quantization algorithms, such as Flash Attention, HIP and CUDA graphs, tensor parallel multi-GPU, GPTQ, AWQ, and token speculation. AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. 1 8B Instruct AWQ in INT4, you will need to have Docker installed (see installation notes) Feb 19, 2025 · 该教程为使用 vLLM 加载 Qwen2. 6是个大版本更新，吞吐量大幅提升 。 在吞吐量和延迟方面进行了优化，与v0. Feb 6, 2025 · 需要8卡80G显存的显卡（一共640G）例如8卡A800 80G或 8卡H800 80G 前置 使用pytorch 2. GPU: MI200s (gfx90a), MI300 (gfx942), Radeon RX 7900 Jan 14, 2025 · succeed inference speed about 3. entrypoints. ︎. However, when I tried the TheBloke/Llama-2-7b-Chat-GPTQ model, it threw the following exception whenever I made a query to the model. 3相比吞吐量提高了1. 7倍 。 vLLM的性能瓶颈主要是由阻塞GPU执行的CPU开销造成的。在vLLM v0. cpp单独部署，或者转换模型格式。 此外，可能存在的误区是认为vLLM支持所有量化格式，但实际上每种框架支持的量化方法不同。需要明确指出vLLM当前支持的量化方法，如AWQ、GPTQ，并说明GGUF的 Aug 31, 2024 · You signed in with another tab or window. Model card Dec 6, 2023 · From my experience, no issue for running vLLM on V100 cards. g, VLLM_CPU_KVCACHE_SPACE=40 means 40 GiB space for KV cache), larger setting will allow vLLM running more requests in parallel. 如果使用hugingface的校准数据集，执行 Mar 4, 2025 · 文章浏览阅读2. Documentation: - casper-hansen/AutoAWQ Feb 19, 2025 · 该教程为使用 vLLM 加载 Qwen2. Feb 21, 2024 · Here's my issue: I have run vllm with both a mistral instruct model and it's AWQ quantized version. I don't know if this 在vLLM中使用AWQ量化模型¶. awq. 8 – 3. 2' quant_path = 'mistral-instruct-v0. Quantization trades off model precision for smaller memory footprint, allowing large models to be run on a wider range of devices. 10 (main, Oct 3 2024, 07:29:13) [GCC 11. vLLM is fast with: State-of-the-art serving throughput 原文链接： 用vllm 0. 8-2. 5-3B-Instruct。 对于每个测试问题，我们使用训练数据检索一组「支持」它的类似问题。 考虑「construct」和「subject」等内容使用一组类似的问题，我们创建了… To run vLLM with Llama 3. . For example, if you have two vLLM instances running on the same GPU, you can set the GPU memory utilization to 0. Dec 27, 2024 · This issue tracks follow up enhancements after initial support for the Deepseek V3 model. updated 2025-03-29. 0-2) Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2. 1) binaries. 04 LTS） Mar 7, 2025 · tl;dr; Using --tensor-parallel-size 2 hangs with both GPUs at 100% utilization and no debug logs explaining anything. Jan 18, 2024 · To run an AWQ model with vLLM, you can use TheBloke/Llama-2-7b-Chat-AWQ with the following command: 要在 vLLM 里运行 AWQ 模型，可以使用下面的命令运行 TheBloke/Llama-2-7b-Chat-AWQ 模型： Feb 22, 2025 · After quantization, the MTP part of the model’s weights are directly discarded (the weights for block 61 in the original model are missing in the AWQ-quantized model). Here is the GPU memory with the mistral model: Mar 19, 2025 · vLLM 是一款专为大语言模型推理加速而设计的框架，实现了 KV 缓存内存几乎零浪费，解决了内存管理瓶颈问题。更多 vLLM 中文文档及教程可访问 →https://vllm. 8 # 设置每张卡显存占用率 --max-model-len 30720 # 模型上下文长度（包含prompt+response） --enforce-eager # 禁用CUDA图，节约显存。 Paper Link👁️. 7. Requirements# OS: Linux. You can quantize your own models by installing AutoAWQ or picking one of the 6500+ models on Huggingface. FP8-E5M2 KV cache. linear. ” 嗯，是一个python库。这解释有些抽象，讲了一些东西，但是还仍然没让人搞懂vLLM是啥。 大致浏览官网文档， 之后看到vLLM . Mar 10, 2025 · #### AWQ量化方案优势 AWQ(Adaptive Weight Quantization)是一种先进的权重压缩算法，它允许神经网络以较低位宽表示而不会明显损失准确性。 对于 Qwen 2 . prompts = ["Hello, 请注意，目前 vllm 中的 awq 支持尚未优化。我们建议使用模型的非量化版本以获得更高的准确性和吞吐量。目前，你可以使用 awq 来减少内存占用。截至目前，它更适合于少量并发请求的低延迟推理。vllm 的 awq 实现比非量化版本具有更低的吞吐量。 在vLLM中使用AWQ量化模型¶. 1+cu124 Is debug build: False CUDA used to build PyTorch: 12. The default max_position_embeddings in config. 8. 0] (64-bit runtime) Python platform: Linux Oct 18, 2023 · Hi @xianwujie, vLLM assumes that the model weights are already stored in the quantized format and the model directory contains a config file for the quantization method. 4 and higher natively supports all Qwen3 and Qwen3MoE models. 5-coder-32b-instruct-AWQ 模型长上下文下（8k)输出结果不正确，且无法正常停止 Jan 13, 2025 Oct 3, 2023 · AWQはvLLMでも最新Verである0. ) mean ↓ Tutorial | Guide GGML and GGUF refer to the same concept, with GGUF being the newer version that incorporates additional data about the model. compile 集成; 自动前缀缓存; 指标; 开发者指南. AWQConfig) [source] # Bases: vllm. AWQ (Automatic Weight Quantization) Memory Usage: Moderate; Precision: Moderate; Description: AWQ automates the quantization process by automatically determining optimal scaling factors for May 6, 2025 · You signed in with another tab or window. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to See the Tensorize vLLM Model script in the Examples section for more information. Same result with Turing. 3 + QWQ32B Q4量化版本功能、性能测试相比于ollama, llama. Parameters: quant_config – The AWQ quantization config. 5 brings the following improvements upon Qwen2: Feb 10, 2025 · vllm v0. vLLM 0. DeepSeek V3 AWQ AWQ of DeepSeek V3. 12！以及miniconda或anaconda pip换清华源 pip config set global. You switched accounts on another tab or window. 4 onwards supports model inferencing and serving on AMD GPUs with ROCm. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing and leave adequate room for model thinking. awq_dequantize这个kernel，这个kernel的代码单独抽出来只有几十行代码，但是代码中涉及到的魔法和数学有点多，如果不了解这里的原理就会很痛苦，所以我这里来详细解析一下。 When using vLLM as a server, pass the --quantization awq parameter, for example: python3 python -m vllm. AWQLinearMethod (quant_config: vllm. vLLM is a fast and easy-to-use library for LLM inference and serving. ai/vLLM 是一个快速且易于使用的库，专为大型语言模型 (LLM) 的推理和部署而设计。 Jun 20, 2023 · @chu-tianxiang I tried forking your vllm-gptq branch and was successful deploying the TheBloke/Llama-2-13b-Chat-GPTQ model. 0] (64-bit runtime) Python platform: Linux Feb 26, 2025 · If you encounter any issues or need advanced usage with lmdeploy, we recommend reading the lmdeploy documentation. … 该教程为在 RTX4090 上使用 vLLM 加载 AWQ 量化 Qwen2. 5 tokens/s with batch size 1 on 8xA100(80GB) 原文： Benchmark: 用vllm自带的工具对 QwQ-32B-AWQ进行压测一、省流，直接看结论一）参数：两个4090，1000 token的输入，128 token的输出（vllm benchmark默认值）1. Quantization reduces the model’s precision from BF16/FP16 to INT4 which effectively reduces the total model memory footprint. 8，我的python为3. Initialization Nov 2, 2023 · AWQ stands for “Activation-aware Weight Quantization”, which is an efficient and accurate low-bit weight quantization (INT3/4) for LLMs. We would recommend using the unquantized version of the model for better accuracy and higher throughput. For Qwen2. Only the plain int4/int8 modes work, which are largely undocumented, and I guess for good reason. edu. 5-VL- Apr 30, 2024 · 首先安装awq包，直接pip 安装即可. int32 的格式存储，供VLLM直接加载推理。. Running DeepSeek-R1-AWQ on single A100 with vllm I discovered an interesting thing, by placing the MOE experts' weights in the CPU's pinned memory during loading, Triton kernels for FusedMOE can support pinned CPU tensors. I've quantized the model myself with AutoAWQ. model_executor. ︎ This compatibility chart is subject to change as vLLM continues to evolve and expand its support for different hardware platforms and AWQ models are also supported directly through the LLM entrypoint: from vllm import LLM, SamplingParams # Sample prompts. Python: 3. vLLM has supported AWQ, which means that you can directly use our provided AWQ models or those quantized with AutoAWQ with vLLM. 23. 请注意，在此步骤中需要将 need_pack 参数设置为 True, 这会将4-bit的权重 打包 为 torch. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. vLLM is fast with: State-of-the-art serving throughput. Mar 29, 2025 · 关于VLLM部署的问题，作者发现不管是官方提供的AWQ模型还是自己量化的AWQ模型推理速度都慢于未量化版，目前还没找到原因；另外就是作者的设备使用VLLM部署时7b-awq不需要加--enforce-eager，但是32b-awq和72b-awq都需要加这个参数，目前推测是显存不够，真实原因有待 Feb 9, 2025 · Your current environment My hardware is 2xA100 (80GB). 1. 9 的 NVIDIA GPU 上受支持，即 Ada Lovelace、Hopper 及更新的 GPU。 Welcome to vLLM#. py:446] Using AWQ quantization with ROCm, but VLLM_USE_TRITON_AWQ is not set, enabling VLLM_USE_TRITON_AWQ. To test the memory usage with several A100 GPUs, we will consider the following variables: the number of GPUs, whether AWQ 4-bit quantization is used, and the size of --cache-max-entry-count. Dec 17, 2024 · Defaulting to 'generate'. 09. From the AWQ paper: AWQ improves over round-to-nearest quantization (RTN) for different model sizes and different bit-precisions. 4 KB vllm serve是启动命令 --tensor-parallel-size 8 # 和卡的数量对应 --trust-remote-code # 是否信任huggingface上的代码 --gpu-memory-utilization 0. 4. They appear to use a single scaling factor per tensor, as described here. Here is the GPU memory with the mistral model: Jun 20, 2023 · @chu-tianxiang I tried forking your vllm-gptq branch and was successful deploying the TheBloke/Llama-2-13b-Chat-GPTQ model. 5-3B-Instruct-AWQ 模型进行少样本学习，包括模型的加载、数据的准备、推理过程的优化，以及结果的提取和评估。 关键步骤是： 使用 vLLM（为了提高速度） 使用 AWQ 4 位量化（以避免 GPU VRAM OOM） Feb 10, 2025 · vllm v0. 5 brings the following improvements upon Qwen2: 当满足以下条件时，Xinference 会自动选择 vLLM 作为推理引擎： 模型格式为 pytorch ， gptq 或者 awq 。 当模型格式为 pytorch 时，量化选项需为 none 。 当模型格式为 awq 时，量化选项需为 Int4 。 当模型格式为 gptq 时，量化选项需为 Int3, Int4 或 Int8 。 2024. 従来の量子化モデルよりもより性能・効率面で優れているそうで、推論の高速化を期待して試してみたいと思います。 vLLM 0. Details I know all the devs are hyper-focused on R1 optimizations right now, which I appreciate. This quant modified some of the model code to fix an overflow issue when using float16. In this case, I used the bitsandbytes compression method. 5-32B-Instruct-AWQ Introduction Qwen2. Efficient management of attention key and value memory with PagedAttention. 1)，新版为AWQ量化模型提升了效率提；不然推理效率可能并为被良好优化（即效率可能较非量化模型低）。 Feb 19, 2025 · 该教程为使用 vLLM 加载 Qwen2. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to Feb 6, 2025 · The past few weeks in the world of open-source AI have been nothing short of extraordinary, marked by the release of Deepseek-R1, Mistral-Small and Qwen2. 1) which brings performance improvements to AWQ models; otherwise, the performance might not be well-optimized. Recommended for AWQ quantization. vLLM’s Plugin System; vLLM Paged Attention; Multi-Modal Data Processing; Automatic Prefix Caching; Python Multiprocessing; V1 Design Documents. If None, we first check the quantization_config attribute in the model config file. Example command: vllm serve Qwen/ --enable-reasoning --reasoning-parser deepseek_r1 All models should work with the command as above. 5-VL deployment and inference. 0中引入了一系列优化，以减小这些开销。 三、vLLM参数配置 Feb 21, 2024 · Here's my issue: I have run vllm with both a mistral instruct model and it's AWQ quantized version. Jul 7, 2023 · SmoothQuant W8A8, it use torch-int and truly INT8 kernel, throughput is higher than current AWQ solution, see Support W8A8 inference in vllm #1508 for more details, it still WIP. - VLLM_ENGINE_PROFILER_WARMUP_STEPS, number of steps to ignore for profiling. Qwen2. See full list on github. jsuper changed the title vllm部署qwen2. 5-coder-32b-instruct-AWQ 模型长上下文下（8k)输出无法正确停止 vllm部署qwen2. tuna. You can also find these compressed models on Hugging Face Feb 9, 2025 · 在安装vLLM时，我们可以使用预编译的二进制文件来加速安装过程： VLLM_USE_PRECOMPILED = 1 pip install-e. 5 Max. At the moment AWQ quantization is not supported in ROCm, but SqueezeLLM quantization has been ported. - VLLM_ENGINE_PROFILER_STEPS, number of steps to capture for profiling. Feb 22, 2025 · ⽣成 vllm 镜像的 dockerfile 示例如下： deepseek-r1-awq 是 671b 的参数，但是精度只有 int4，单台 8 卡 A100 可以完成部署。 vLLM is a fast and easy-to-use library for LLM inference and serving. The AWQ model works on TP=1 with 1 A100, but the performance is very bad and slow (~2 token/s) while using V1. layers. Installing vLLM# Run the following commands to build a Docker image vllm-rocm. vLLM is fast with: State-of-the-art serving throughput For those who don't know what different model formats (GGUF, GPTQ, AWQ, EXL2, etc. vLLM’s AWQ implementation have lower throughput than unquantized version. 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. compile integration; Automatic Prefix Caching; Metrics; Developer Guide. index-url https://mirrors. Quantization reduces the bit-width of model weights, enabling efficient model serving with class vllm. Quantized by Eric Hartford and v2ray. 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。 Oct 26, 2024 · vllm与awq量化的结合，使得模型在保证高效响应的同时，不会因为量化而降低模型生成内容的质量。 AWQ量化减少了模型推理过程中的计算量，而VLLM的高效并行处理则进一步减少了模型的响应时间，两者的结合使得实时交互应用更加流畅。 See the Tensorize vLLM Model script in the Examples section for more information. 为了确保vLLM能够正常运行，我们需要安装一些必要的依赖库。 同样，如果未来vLLM的官方仓库合并了相关支持，直接使用pip install vllm即可。 Sep 23, 2023 · vllm项目llama2-awq量化速度测试 使用了vllm项目其中的两种方式，是api方式和直接生成的方式，分别在： benchmark/benchmark INT8 W8A8#. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. 5 tokens/s with batch size 1 on 8xA100(80GB) 前言 vLLM对于一些同学来说可能比较陌生，它是个什么东西？检索一下，找到了它的官方文档，里面介绍说：“vLLM is a Python library that also contains pre-compiled C++ and CUDA (12. tsinghua. 👍 9 NaiveYan, Valdanitooooo, mrFranklin, Linnore, humbinal, WhiteGiver-Plus, xuhonghui, QwertyJack, and erickech reacted with thumbs up emoji Nov 13, 2023 · For AWQ, we will use the vLLM package as that was, at least in my experience, the road of least resistance to using AWQ: pip install vllm With vLLM, loading and using our model becomes painless: Nov 11, 2024 · 【2】在vLLM中使用AWQ量化模型. To use GPTQ models you need to install the autoGTPQ and optimum libraries pip install auto-gptq optimum. Continuous batching of incoming requests. 1 8B Instruct AWQ in INT4, you will need to have Docker installed (see installation notes) Quantization#. Prefix-caching. It is also now supported by continuous batching server vLLM, allowing the use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Mar 29, 2025 · 关于VLLM部署的问题，作者发现不管是官方提供的AWQ模型还是自己量化的AWQ模型推理速度都慢于未量化版，目前还没找到原因；另外就是作者的设备使用VLLM部署时7b-awq不需要加--enforce-eager，但是32b-awq和72b-awq都需要加这个参数，目前推测是显存不够，真实原因有待 We would recommend using the unquantized version of the model for better accuracy and higher throughput. 为 vLLM 做贡献; vLLM 性能分析; Dockerfile; 添加新模型. You can use a tool like AutoAWQ to quantize your fine-tuned mod Feb 9, 2025 · Your current environment My hardware is 2xA100 (80GB). 1)，新版为AWQ量化模型提升了效率；不然推理效率可能并为被良好优化（即效率可能较非量化模型低）。 实际上，使用AWQ模型与vLLM的基本用法 Mar 7, 2025 · 硬件：单卡4090 目前使用的参数 vllm serve Qwen/QwQ-32B-AWQ --quantization awq \ --enable-reasoning --reasoning-parser deepseek_r1 … Mar 9, 2025 · ©著作权归作者所有,转载或内容合作请联系作者 平台声明：文章内容（如有图片或视频亦包括在内）由作者上传并发布，文章内容仅代表作者本人观点，简书系信息发布平台，仅提供信息存储服务。 Feb 10, 2025 · 一、硬件与系统环境要求 1. LinearMethodBase. I have uploaded the Qwen2. vLLM 的 torch. com Dec 5, 2024 · In this blog post, we explore AWQ, a novel weight-only quantization technique integrated with vLLM. 5 to 72 billion parameters. vLLM已支持AWQ可以使用AWQ量化模型或使用AutoAWQ量化的模型。建议使用最新版的vLLM (vllm>=0. vLLM已支持AWQ，您可以直接使用我们提供的AWQ量化模型或使用 AutoAWQ 量化的模型。 我们建议使用最新版的vLLM (vllm>=0. 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. To serve using vLLM with 8x 80GB GPUs, use the following command: Mar 7, 2025 · 运行服务安装 vllm下载模型请求调用curl - completionscurl - chat/completionsPython - completionPython - chat completion_vllm qwq 实战 - vLLM部署 QwQ-32B-AWQ [单卡4090] 编程乐园 已于 2025-03-07 13:47:48 修改 Apr 18, 2024 · AWQ量化方法介绍请参考： 吃果冻不吐果冻皮：大模型量化技术原理-AWQ、AutoAWQ本文是将huggingface格式的模型weights进行模型转换。并提供在vLLM的运行命令。 安装AWQpip3 install autoawq -i https://pypi. Easy, fast, and cheap LLM serving for everyone Star Watch Fork. Aug 16, 2024 · 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。 Jan 11, 2024 · You signed in with another tab or window. 4 ROCM used to build PyTorch: N/A OS: CentOS Stream 9 (x86_64) GCC version: (GCC) 11. . 6和python3. It does not matter if you have another vLLM instance running on the same GPU. Data types currently supported in ROCm are FP16 and BF16. Please feel free to chime in and contribute! Follow up [Model] [Quantization] Support deepseek_v3 w8a8 fp8 block-wise quantization #11523: enhance testing with shapes of production models and run it regularly on H100. prompts = ["Hello, 2024. 0 20240719 (Red Hat 11. WARNING 12-17 21:04:57 config. This quantization method is particularly useful for reducing model size while maintaining good performance. 1)，新版为AWQ量化模型提升了效率提；不然推理效率可能并为被良好优化（即效率可能较非量化模型低）。 We would recommend using the unquantized version of the model for better accuracy and higher throughput. api_server --model TheBloke/law-LLM-AWQ --quantization awq --dtype half Note: at the time of writing, vLLM has not yet done a new release with support for the quantization parameter. hyper. The main benefits are lower latency and memory usage. Currently, you can use AWQ as a way to reduce memory footprint. 5 for each instance cognitivecomputations / DeepSeek-R1-awq. ikmdx ancpf idsy kdfw ckayxt efbz jpdu nfy acdxel otadj