本頁面中的部分或全部資訊可能不適用於 Trusted Cloud by S3NS。
關於 Trusted Cloud by S3NS 上的 GPU
Trusted Cloud by S3NS 專注於提供世界級的人工智慧 (AI) 基礎架構，在各個領域提供最嚴苛的 GPU 加速工作負載。您可以使用 GPU 在 Trusted Cloud by S3NS 上執行 AI、機器學習 (ML)、科學、分析、工程、消費性和企業應用程式。
透過與 NVIDIA 的合作， Trusted Cloud by S3NS 可提供最新的 GPU，同時透過各種儲存空間和網路選項，對軟體堆疊進行最佳化。如需可用 GPU 的完整清單，請參閱「GPU 平台」。
以下各節將概述在 Trusted Cloud by S3NS上使用 GPU 的優點。
GPU 加速 VM
在 Trusted Cloud by S3NS上，您可以根據需求，以最合適的方式存取及配置 GPU。我們提供專屬的加速器最佳化機器系列，內建 GPU 和網路功能，可盡可能提高效能。這些機器系列包括 A4X、A4、A3、A2 和 G2。
多種佈建選項
您可以使用加速器最佳化機器系列，搭配下列任何開放原始碼或 Trusted Cloud by S3NS 產品來佈建叢集。
Vertex AI
Vertex AI 是全代管機器學習 (ML) 平台，可用於訓練及部署 ML 模型和 AI 應用程式。在 Vertex AI 應用程式中，您可以使用 GPU 加速 VM，以下列方式提升效能：
Cluster Director
Cluster Director (舊稱 Hypercompute Cluster) 是一組功能和服務，可讓您部署及管理大量 (最多數萬個) 加速器和網路資源，這些資源會以單一同質單元運作。這個選項非常適合用來建立密集配置的基礎架構，以便達到最佳效能，並整合 Google Kubernetes Engine (GKE) 和 Slurm 排程器。叢集總管可協助您建構專門用於執行 AI、機器學習和 HPC 工作負載的基礎架構。詳情請參閱「叢集總監」。
如要開始使用 Cluster Director，請參閱「選擇部署策略」。
Compute Engine
您也可以在 Compute Engine 上建立及管理個別 VM 或小型 VM 叢集，並附加 GPU。這個方法主要用於執行圖像密集型工作負載、模擬工作負載或小規模 ML 模型訓練。
下表列出可用來建立已連接 GPU 的 VM 的方法：
Cloud Run
您可以為 Cloud Run 執行個體設定 GPU。GPU 非常適合在 Cloud Run 上使用大型語言模型執行 AI 推論工作負載。
如要在 Cloud Run 上使用 GPU 執行 AI 工作負載，請參閱下列資源：
除非另有註明，否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權，程式碼範例則為阿帕契 2.0 授權。詳情請參閱《Google Developers 網站政策》。Java 是 Oracle 和/或其關聯企業的註冊商標。
上次更新時間：2025-08-18 (世界標準時間)。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-08-18 (世界標準時間)。"],[[["\u003cp\u003eGoogle Cloud provides robust GPU infrastructure to support demanding AI, machine learning, scientific, and enterprise workloads, with optimized software and networking options.\u003c/p\u003e\n"],["\u003cp\u003eUsers can leverage GPU-accelerated virtual machines (VMs) via accelerator-optimized machine families like A3, A2, and G2, designed for maximum performance.\u003c/p\u003e\n"],["\u003cp\u003eVertex AI offers various ways to utilize GPU-enabled VMs to improve model training, deployment, and prediction latency, along with supporting open-source large language models.\u003c/p\u003e\n"],["\u003cp\u003eHypercompute Cluster enables the creation of clusters of GPU-accelerated VMs, managed as a single unit, suited for AI, ML, and high-performance computing (HPC) workloads.\u003c/p\u003e\n"],["\u003cp\u003eCompute Engine provides options to create individual VMs or small clusters with attached GPUs for graphics-intensive tasks and small-scale model training, while Cloud Run supports GPU configuration for AI inference workloads.\u003c/p\u003e\n"]]],[],null,["# About GPUs on Google Cloud\n\n*** ** * ** ***\n\nGoogle Cloud is focused on delivering world-class artificial intelligence (AI)\ninfrastructure to power your most demanding GPU-accelerated workloads across a\nwide range of segments. You can use GPUs on Google Cloud to run AI, machine\nlearning (ML), scientific, analytics, engineering, consumer, and enterprise\napplications.\n\nThrough our partnership with NVIDIA, Google Cloud delivers the latest GPUs while\noptimizing the software stack with a wide array of storage and networking\noptions. For a full list of GPUs available, see [GPU platforms](/compute/docs/gpus).\n\nThe following sections outline the benefits of GPUs on Google Cloud.\n\nGPU-accelerated VMs\n-------------------\n\nOn Google Cloud, you can access and provision GPUs in the way that best suits\nyour needs. A specialized [accelerator-optimized machine family](/compute/docs/accelerator-optimized-machines) is\navailable, with pre-attached GPUs and networking capabilities that are ideal for\nmaximizing performance. These are available in the A4X, A4, A3, A2, and G2\nmachine series.\n\nMultiple provisioning options\n-----------------------------\n\nYou can provision clusters by using the accelerator-optimized machine family\nwith any of the following open-source or Google Cloud products.\n\n### Vertex AI\n\nVertex AI is a fully-managed machine learning (ML) platform that you\ncan use to train and deploy ML models and AI applications. In Vertex AI\napplications, you can use GPU-accelerated VMs to improve performance in the\nfollowing ways:\n\n- [Use GPU-enabled VMs](/vertex-ai/docs/training/configure-compute) in custom training GKE worker pools.\n- Use [open source LLM models from the Vertex AI Model Garden](/vertex-ai/generative-ai/docs/open-models/use-open-models).\n- Reduce [prediction](/vertex-ai/docs/predictions/configure-compute#gpus) latency.\n- Improve performance of [Vertex AI Workbench](/vertex-ai/docs/workbench/instances/change-machine-type) notebook code.\n- Improve performance of a [Colab Enterprise runtime](/colab/docs/create-runtime-template).\n\n### Cluster Director\n\nCluster Director (formerly known as *Hypercompute Cluster* ) is a set of\nfeatures and services that are designed to let you deploy and manage large\nnumbers, up to tens of thousands, of accelerator and networking resources that\nfunction as a single homogeneous unit. This option is ideal for creating a\ndensely allocated, performance-optimized infrastructure that has integrations\nfor Google Kubernetes Engine (GKE) and Slurm schedulers. Cluster Director helps\nyou to build an infrastructure that is specifically designed for running AI, ML,\nand HPC workloads. For more information, see [Cluster Director](/ai-hypercomputer/docs/hypercompute-cluster).\n\nTo get started with Cluster Director, see [Choose a deployment strategy](/ai-hypercomputer/docs/choose-strategy).\n\n### Compute Engine\n\nYou can also create and manage individual VMs or small clusters of VMs with\nattached GPUs on Compute Engine. This method is mostly used for running\ngraphics-intensive workloads, simulation workloads, or small-scale ML model\ntraining.\n\nThe following table shows the methods that you can use to create VMs that have\nGPUs attached:\n\n### Cloud Run\n\nYou can configure GPUs for your Cloud Run instances. GPUs are ideal for\nrunning AI inference workloads using large language models on Cloud Run.\n\nOn Cloud Run, consult these resources for running AI workloads on GPUs:\n\n- [Configure GPUs for a Cloud Run service](/run/docs/configuring/services/gpu)\n- [Load large ML models on Cloud Run with GPUs](/run/docs/configuring/services/gpu-best-practices#model-loading-recommendations)\n- [Tutorial: Run LLM inference on Cloud Run GPUs with Ollama](/run/docs/tutorials/gpu-gemma2-with-ollama)"]]
