HPE announced the next generation of compute with high core density, power efficiency, and workload performance with up to 128 core count per-socket delivering high performance and power efficiency.
The HPE ProLiant RL300 Gen11 features the Ampere® Altra® and Ampere® Altra® Max processors designed for predictable frequency scaling to lessen the impact of problems like noisy neighbors. Performance for traditional applications has often been measured by average latencies, but cloud-native workloads with high levels of inter-process communication require more granular latency management.
A large CPU core count allows for higher datacenter density, allowing service providers to extract even more revenue out of their CapEx. With high power efficiency, businesses can eliminate underutilized rack space in their datacenter with a platform that can also be deployed into power-constrained edge environments.
AI Inference Server
GPU-Free AI Inference on the HPE RL300 ProLiant Gen11
The HPE ProLiant RL300 Gen11 Server for AI Inference is the first AI platform in the HPE ProLiant family that delivers leading AI Inference performance without the use of GPUs.
Learn More
Visit the Ampere AI homepage for more information about Ampere’s innovations in this space and for access to our software packages: AI Solutions Home
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Arm saves cost and power for EDA with HPE RL300 Gen11 - Infrastructure Solutions blog - Arm Community blogs - Arm Community
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HPE ProLiant RL300 Gen11 server certified with Oracle Linux to help customers deliver cloud native solutions
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Jan, 2023
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A Deep-Dive into HPE ProLiant RL300 Gen11 Performance Leadership
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Gorilla Guide - How HPE ProLiant RL300 Gen11 Delivers Density Efficiency and a Reduced Footprint
Aug, 2024
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Next generation of flexible, efficient, cloud-native compute
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Power Saving for Java Applications using Ampere servers
Nov, 2023
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The HPE ProLiant RL300 Gen11
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HPE ProLiant RL300 Gen11, compute engineered for a hybrid world: scale-out performance
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Get scale-out performance with unprecedented efficiency
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Game-changing efficiency for running cloud-native workloads
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