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Ampere AI Efficiency: Recommender Engine

Efficient AI Recommender Engine Workloads

Energy Demand of Recommender Engine Workloads

Recommender engines, a common AI function used in e-commerce, content streaming, and personalized marketing, require the processing of vast amounts of user data to generate personalized recommendations. Efficient handling of the large-scale data processing and real-time recommendation generation is necessary. Hardware buyers, service owners, and infrastructure planners need solutions that can handle the high data volumes and perform quick and accurate computations to deliver seamless user experiences.

In the context of escalating energy demands and the urgent need for enhanced infrastructure efficiency, it is crucial to analyze and optimize the hardware components responsible for recommender engine workloads. By reducing energy consumption without compromising performance, businesses can achieve substantial cost savings, minimize environmental impact, and improve overall system efficiency.

Key Benefits of Ampere Cloud Native Processors for Handling Recommender Engine Workloads

Best Performance at the Rack Level:

Ampere cloud native processors offer unique advantages in performance at the rack level, specifically tailored for running recommender engine workloads. With their optimized designs and efficient architectures, these processors deliver exceptional compute power, enabling enterprises to handle complex machine learning algorithms and generate real-time recommendations at scale.
The high-performance capabilities of Ampere cloud native processors ensure faster processing, reduced latency, and improved response times, empowering businesses to deliver personalized and engaging user experiences while maintaining the scalability and reliability required for recommender engine workloads in data center environments.

Superior Energy Efficiency for AI Inference:

Energy efficiency is crucial in AI inference workloads, where large-scale computations are performed continuously. Ampere Cloud Native Processors excel in this area, providing superior energy efficiency compared to legacy x86 architecture processors. The efficient design and power optimization techniques of Ampere processors ensure that AI inference tasks can be executed with minimal power consumption, reducing operational costs and enabling more sustainable data center operations.

Scalability and Flexibility for AI Workloads:

Ampere processors provide scalability and flexibility to meet the evolving demands of AI inference workloads. With their native support for cloud-native architectures, containerization, and microservices, Ampere processors enable practitioners to scale AI resources seamlessly. This scalability empowers enterprises to handle increasing AI workloads, accommodate rapid growth, and deploy dynamic AI applications efficiently.

Performance at the Rack Level and Performance per Watt - Competitive Comparison

Background on the Benchmarked Recommender Engine Model: DLRM_Torchbench

DLRM_Torchbench is an innovative deep learning recommendation model designed to revolutionize the way enterprises leverage AI for their recommender engine workloads. Developed by leading AI experts, DLRM_Torchbench brings cutting-edge advancements in machine learning to enable businesses to deliver highly personalized and accurate recommendations, enhancing user experiences and driving revenue growth.

Comparative Results

On the measure of performance/rack Ampere Cloud Native Processors provide up to 239% better performance for running recommender engine workloads when compared to the legacy x86 architecture data center processors offered by AMD and Intel.

Performance/Rack 1S(DLRM Throughput)

Ampere Cloud Native Processors deliver unparalleled performance that drives innovation, accelerates data processing, and supports the most demanding AI workloads. With Ampere's processors, enterprises can unleash the full potential of their data center infrastructure and achieve new levels of performance, scalability, and responsiveness in the modern digital landscape, while keeping in check energy-consumption.

Recommender Engines: Industry Verticals Use Cases

Recommender engine workloads are prevalent across various industry verticals, significantly impacting user engagement, satisfaction, and revenue generation. Key sectors that commonly rely on recommender engine workloads include:

E-commerce and Retail: Online marketplaces depend on recommender engines to drive sales and enhance customer experience by suggesting relevant products based on user preferences and historical behavior. Personalized product recommendations increase conversion rates and foster customer loyalty.

Media and Entertainment: Content streaming platforms utilize recommender engines to curate personalized playlists, recommend movies or TV shows, and improve content discovery. By understanding user preferences, these platforms enhance user retention and engagement, resulting in longer viewing sessions.

Advertising and Marketing: Recommender engines enable targeted and personalized advertising by suggesting relevant products or services based on user interests and browsing history. This high level of personalization improves campaign performance, click-through rates, and return on investment.

Travel and Hospitality: Recommender engine workloads find practical applications in the travel industry by suggesting personalized travel itineraries based on user preferences and historical data. This enhances the travel experience, providing tailored recommendations and increasing customer satisfaction.

By optimizing infrastructure, businesses can maximize the efficiency of recommender engine workloads, reduce energy consumption, and ensure a more sustainable infrastructure.

Future-proof your Enterprise and Innovate with Ampere Cloud Native Processors

Ampere Cloud Native Processors represent the future of data center infrastructure. Designed to harness emerging technologies, such as AI, machine learning, and edge computing, Ampere processors empower businesses to capitalize on the latest advancements and gain a competitive edge. By adopting Ampere processors, enterprises position themselves at the forefront of innovation, ready to adapt to evolving industry trends and drive digital transformation with confidence, while mitigating any concerns surrounding the rising energy costs of maintaining the expanding compute infrastructure.

For more information on Ampere Solutions for AI

Visit, to learn about Ampere offering for AI. Download the Ampere Optimized AI Frameworks directly from the website free of charge or find out about our alternative AI software distribution channels. Benefit from 2-5 x additional raw performance provided by Ampere Optimized AI Frameworks (already included in the comparative presented benchmarks). You can reach the Ampere AI team directly at for any inquiries on running your specific recommender engine workloads.


All data and information contained herein is for informational purposes only and Ampere reserves the right to change it without notice. This document may contain technical inaccuracies, omissions and typographical errors, and Ampere is under no obligation to update or correct this information. Ampere makes no representations or warranties of any kind, including but not limited to express or implied guarantees of noninfringement, merchantability, or fitness for a particular purpose, and assumes no liability of any kind. All information is provided “AS IS.” This document is not an offer or a binding commitment by Ampere. Use of the products contemplated herein requires the subsequent negotiation and execution of a definitive agreement or is subject to Ampere’s Terms and Conditions for the Sale of Goods.

System configurations, components, software versions, and testing environments that differ from those used in Ampere’s tests may result in different measurements than those obtained by Ampere.

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Created At : August 16th 2023, 11:01:55 am
Last Updated At : February 14th 2024, 5:43:23 pm
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