max_model_len=32000, gpu_memory_utilization=0.8, n_gpu=8) 是因为还需要其它的配置参数吗?能否出一份文档详细介绍下部署模型时可能用到的各个参数? 肯定是走了,8张卡的显存都快占满了,占用比例和gpu_memory_utilization=0.8基本一致 会不会和vllm版本有关系? 我在另一台服务器上用0.4.0版本
'trust_remote_code': True,'tensor_parallel_size': 2,'block_size': 16,'swap_space': 4,'gpu_memory_utilization': 0.9,'max_num_seqs': 256,'quantization': None,'max_model_len': 4096}Enable lora: False. Lora count: 0.
值得注意的是,VLLM会默认将显卡的全部显存预先申请以提高缓存大小和推理速度,用户可以通过参数gpu_memory_utilization控制缓存大小。 首先安装VLLM: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 pip install vllm 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import os os.environ['VLLM_USE_MODEL...
另外对于同一个句子生成多个回答的情况,VLLM会将不同的逻辑块映射为一个物理块,起到节省显存提高吞吐的作用。 值得注意的是,VLLM会默认将显卡的全部显存预先申请以提高缓存大小和推理速度,用户可以通过参数gpu_memory_utilization控制缓存大小。 首先安装VLLM: pip install vllm import os os.environ['VLLM_USE_MO...
The simplest way to improve GPU utilization, and effectively throughput, is through batching. Since multiple requests use the same model, the memory cost of the weights is spread out. Larger batches getting transferred to the GPU to be processed all at once will leverage more of the compute av...
(TensorFlow, PyTorch, TensorRT Plan, Caffe, MXNet, or custom) from local storage, the Google Cloud Platform, or AWS S3 on any GPU- or CPU-based infrastructure. It runs multiplemodelsconcurrently on a single GPU to maximize utilization and integrates with Kubernetes for orchestration, metrics, ...
值得注意的是,VLLM会默认将显卡的全部显存预先申请以提高缓存大小和推理速度,用户可以通过参数gpu_memory_utilization控制缓存大小。 首先安装VLLM: pip install vllm import os os.environ['VLLM_USE_MODELSCOPE'] = 'True' from vllm import LLM, SamplingParams prompts = [ "Hello, my name is", "The ...
This section focuses on understanding latency and throughput tradeoffs for a single model. TheModel Analyzersection describes a tool that helps you understand the GPU memory utilization of your models so you can decide how to best run multiple models on a single GPU. ...
值得注意的是,VLLM会默认将显卡的全部显存预先申请以提高缓存大小和推理速度,用户可以通过参数gpu_memory_utilization控制缓存大小。 首先安装VLLM: pipinstallvllm 1. importos os.environ['VLLM_USE_MODELSCOPE']='True'from vllmimportLLM, SamplingParams ...
Multiblock attention for long sequences: Addressing the challenge of long input sequences, TensorRT-LLM multiblock attention maximizes GPU utilization by distributing tasks across streaming multiprocessors (SMs). This technique improves system throughput by more than 3x, enabling support for larger context...