T4 將 GPU 加速帶入全球主要企業伺服器。
NVIDIA T4 企業 GPU 和CUDA-X 加速函式庫可讓專為當今現代資料中心而設計的主流伺服器展現極致性能。NGC-Ready 驗證讓這些伺服器能輕鬆處理所有加速工作負載。透過NVIDIA NGC 支援服務提供的選用支援，讓你無後顧之憂。NVIDIA T4 可以讓 IT 團隊利用全球主要的伺服器打造標準 GPU 加速資料中心基礎架構。
虛擬桌面基礎架構 (VDI)* 效能提升 33%
機器學習訓練速度提升 35 倍
深度學習訓練訓練速度提升 10 倍
深度學習推論獲得深入剖析的速度提升 40 倍
產品設定資訊
儘管深度學習近期才加入人工智慧市場，許多家公司都早已利用機器學習蒐集深入剖析資料達數十年。NVIDIA T4 資料中心 GPU 可利用 CUDA-X AI 應用軟體加速函式庫 RAPIDS，一組可供 GPU 加速資料準備與機器學習的開放原始碼程式庫，來加速這些機器學習技能。相較於僅使用 CPU 的伺服器，包含 XGBoost、PCA、K-means、k-NN、DBScan 和 tSVD 等演算法，透過 Python 等熟悉的開發工具，T4 GPU 可加速機器學習速度達 35 倍。
NVIDIA T4 以革命性 NVIDIA Turing™ Tensor 核心技術為基礎，配備針對人工智慧工作負載打造的多精度運算。T4 支援從 FP32 進展到 FP16 再到 INT8 的突破性效能以及 INT4，推論效能比 CPU 高 40 倍。在訓練方面，搭載兩顆 T4 GPU 的單一伺服器可取代配備九個雙插槽、僅使用 CPU 的伺服器。 開發人員可直接透過 NVIDIA CUDA-X AI 應用軟體函式庫與所有人工智慧框架的整合，發揮 Turing Tensor 核心的強大功能。CUDA-X 程式庫以 NVIDIA 平行程式化模型 CUDA 為基礎，為人工智慧、智慧機器、高效能運算與繪圖計算所需的特定運算最佳化。
配備 NVIDIA T4 的主流伺服器 經 NVIDIA 合作夥伴認證適用於虛擬化 GPU 應用軟體，確保使用者無論在何處、使用何種裝置，都能享有高品質的虛擬桌面體驗。從專業知識從業人員和行動專業人員到設計師和工程師，只要將 T4 與 NVIDIA 虛擬化 GPU 應用軟體 (NVIDIA GRID® 虛擬化 OA 效能電腦 (GRID vPC) 和 NVIDIA Quadro® 虛擬 3D 繪圖工作站 (Quadro vDWS)) 搭配使用，即可加速 VDI 體驗，提供比僅使用 CPU 的 VDI 環境高出 33% 的效能。
若要最佳化應用軟體以獲得世界級效能，需要深入瞭解許多不同的應用軟體層級，包含作業系統、驅動程式、程式庫、框架與應用程式。NGC 容器為預先內建且針對 GPU 運算高度最佳化。它們以 GPU 加速 深度學習框架、RAPIDS 應用軟體、NVIDIA TensorRT™和 600 多個高效能運算應用程式提供 NVIDIA 無可比擬的專業知識與生態系統支援。 NVIDIA NGC-Ready 程式可供所有伺服器開發人員在系統上驗證 NGC 容器。NGC-Ready 伺服器讓資料中心管理人員可放心部署配備 GPU 的伺服器，快速設定以輕鬆處理各種加速應用程式。
VDI: GRID vPC tested on a server with 2x Intel Xeon Gold 6148 (20c, 2.4 GHz), GRID vPC with T4-1B (64 VMs), VMware ESXi 6.7, NVIDIA vGPU Software (410.91/412.16), Windows 10 (1803), 2 vCPUs, 4GB RAM, Resolution 1920x1080 resolution, Single Monitor, VMware Horizon 7.6 User experience was measured using an NVIDIA internal benchmarking tool which measured remoted frames running office productivity applications such as Microsoft PowerPoint, Word, Excel, Chrome, PDF viewing and video playback.
Machine Learning: CPU nodes (61 GB of memory, 8 vCPUs, 64-bit platform), Apache Spark. 200 GB CSV dataset; Data preparation includes joins, variable transformations. GPU Server Config: Dual-Socket Xeon E5-2698 v4@3.6GHz, 20 T4 GPUs on 5 nodes, each with 4 T4 GPUs. All run on InfiniBand network, CPU data for XGBoost and Data Conversion steps are estimated based on measured data for 20 CPU nodes, and reducing execution time by 60% to normalize for training on smaller data set on T4.
Deep Learning Training and Inference: GPU: Dual-Socket Xeon E5-2698 v4@3.6GHz. GPU Servers: 2xT4s for Training, 1xT4 for Inference, NGC 18.11-py3 Container with CUDA 10.0.130; NCCL 2.3.7, cuDNN 7.4.1.5; cuBLAS 10.0.130 | NVIDIA Driver: 384.145.
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