Qualcomm Accelerators for HPE
Curious about improving your 5G network? The Qualcomm Accelerators for HPE enhance 5G network infrastructure by providing high-performance, low-latency, and power-efficient capabilities to simplify the adoption of virtualized RAN platforms by operators and infrastructure vendors. These PCIe inline accelerator cards seamlessly integrate into standard Commercial-Off-The-Shelf (COTS) servers, offloading CPUs from latency-sensitive and compute-intensive 5G baseband L1/Physical Layer functions, like demodulation, beamforming channel coding, and Massive MIMO computation needed for high-capacity deployments. Qualcomm Accelerators for HPE are O-RAN compliant and include an upgradable L1 software architecture, certifying adaptability to future 3GPP release features, extending the platform's lifespan, and reducing total cost of ownership.
適用於 HPE 產品的 Qualcomm Cloud AI 100 加速器
您的組織是否需要最佳化的基礎架構,以便在邊緣部署 AI/ML 訓練模型? 適用於 HPE 產品的 Qualcomm Cloud AI 100 加速器可大幅提升伺服器在 AI/ML 推論工作負載方面的效能,從而在電腦視覺、自然語言和其他最先進的產業 AI/ML 使用案例上快速獲得洞察並實現更精確的資料轉換。這款基於 Qualcomm AI 核心、專為推論量身打造的伺服器加速器,支援模型自訂和量化,可降低精度資料類型,從而實現最佳化效能和業界領先的電源效率。憑藉低功耗,適用於 HPE 產品的 Qualcomm Cloud AI 100 加速器可以提高效能並降低營運成本。
適用於 HPE 產品的 NVIDIA 加速器
Do you require higher performance for artificial intelligence (AI) training and inference, high-performance computing (HPC) or graphics? NVIDIA® Accelerators for HPE help solve the world’s most important scientific, industrial, and business challenges with AI and HPC. Visualize complex content to create cutting-edge products, tell immersive stories, and reimagine cities of the future. Extract new insights from massive datasets. Hewlett Packard Enterprise servers with NVIDIA accelerators are designed for the age of elastic computing, providing unmatched acceleration at every scale.
適用於 HPE 產品的 Intel 加速器
When deploying GPUs in a high-performance computing (HPC) environment, customers face substantial obstacles and inefficiencies caused by the need to port and refactor code. Their efforts are further hampered by proprietary GPU programming environments that prohibit portability between GPU vendors and often result in inconsistency between CPU and GPU implementations. The need for GPU-level memory bandwidth, at scale, and sharing code investments between CPUs and GPUs for running a majority of the workloads in a highly parallelized environment has become essential.
Intel Data Center GPU Max Series is designed for breakthrough performance in data-intensive
computing models used in AI and HPC.
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