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.
Does your organization require optimized infrastructure for deploying AI/ML trained models at the edge? The Qualcomm Cloud AI 100 Accelerator for HPE dramatically improves server performance on AI/ML inference workloads for faster insights and higher accuracy data transformations on computer vision, natural language, and other state-of-the-industry AI/ML use cases. Based on the Qualcomm AI Core, this server accelerator is tailored for inference, supporting model customization and quantization to lower precision datatypes for optimized performance and industry-leading power efficiency. With low power consumption, the Qualcomm Cloud AI 100 Accelerator for HPE can increase performance while lowering operating cost.
Do you need optimized acceleration in your data center?
Xilinx® Accelerators for HPE are designed to dramatically increase performance in HPE servers across cloud and on-premises data centers to help accelerate the workloads running in a hybrid cloud environment. Experience breakthrough performance improvements with low latency while running key data center applications such as real-time machine learning, training, inference, video transcoding and encoding, genomics, and data analytics. This series features two flexible options, U50 and U250, to help optimize and adapt to ever-changing business environments.
Deliver significant performance advantages for workloads such as financial computing, machine learning, computational storage, and data search and analytics. Xilinx® Accelerators for HPE are adaptable to changing acceleration requirements and algorithm standards, capable of accelerating any workload without changing hardware, and reduces overall cost of ownership.
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.
AMD accelerators offer exceptional compute performance and performance-per-watt to help accelerate machine intelligence and HPC workflows (Instinct) found in academic and government clusters, oil and gas industries, and deep neural networks.
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.
* Prices may vary based on local reseller.
