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Ampere AI Efficiency: Natural Language Processing

Efficient Processing of AI NLP Workloads

Energy Demand of Natural Language Processing (NLP) Workloads

Natural language processing (NLP) has emerged as a transformative force, enabling machines to understand and interact with humans through language. As the demand for NLP-driven solutions skyrockets, the need for efficient and scalable infrastructure has become increasingly paramount. Servers operating in public cloud, private cloud, and AI services face immense pressure to deliver real-time, accurate, and context-aware NLP inferences. To address these challenges, optimizing energy efficiency at the infrastructure level is crucial.

NLP workloads rely on complex machine learning models such as recurrent neural networks (RNNs), transformers, and deep learning architectures like BERT (Bidirectional Encoder Representations from Transformers). These models require significant computational power during the inference stage, where text inputs are processed and meaningful responses are generated. Ensuring optimal performance in NLP inference workloads while managing energy consumption is essential to meet the growing demands of modern applications.

Key Benefits of Ampere Cloud Native Processors for Handling NLP Workloads

Superior NLP Performance

Ampere processors, built on a custom ARM architecture, outshine Intel and AMD processors based on the legacy x86 architecture when it comes to NLP workloads. The advanced design of Ampere processors incorporates key architectural enhancements, including wider vector units, improved memory bandwidth, and optimized instruction sets tailored for NLP tasks. These optimizations result in faster and more efficient execution of NLP algorithms, enabling industry practitioners to achieve higher throughput and reduced latency for their NLP applications.

Energy Efficiency

Ampere processors offer remarkable energy efficiency advantages over competing Intel and AMD processors. Leveraging a power-efficient design, they provide superior performance per watt, enabling industry practitioners to achieve higher NLP performance while keeping energy consumption in check. This efficiency not only reduces operational costs but also contributes to a greener and more sustainable infrastructure for data centers, allowing to keep operational costs in check as well as aligning with the increasing focus on environmental responsibility.

Cost Efficiency

Cloud native processors offer cost advantages by optimizing resource utilization. They can perform NLP tasks more efficiently, resulting in reduced computational requirements and lower infrastructure costs. With the ability to handle more workloads per unit of computation, businesses can achieve higher efficiency and cost savings.

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

Background on the Benchmarked NLP Model: BERT Base C-Squad

The BERT Base C-Squad model is an advanced deep learning model designed specifically for question answering tasks. It delivers remarkable accuracy and efficiency in understanding and responding to user questions.

Comparative Results

On the measure of performance/rack Ampere Cloud Native Processors provide up to 173% better performance for running NLP workloads compared to the legacy x86 architecture data center processors offered by AMD and Intel.

Performance/Rack 1S(BERT Throughput)

Ampere Cloud Native Processors redefine performance, empowering enterprises to drive innovation, accelerate data processing, and tackle even the most demanding AI workloads. With Ampere's cutting-edge processors, businesses can unlock the full potential of their data center infrastructure, achieving unparalleled levels of performance, scalability, and responsiveness in today's digital landscape—all while optimizing energy consumption.

NLP: Industry Verticals Use Cases

E-commerce and Retail:

  • Sentiment analysis of customer reviews to understand product feedback and improve customer satisfaction.
  • Product categorization and recommendation systems to personalize product suggestions based on user preferences.
  • Chatbots and virtual assistants for customer support, helping users with product queries and order tracking.

Media and Entertainment:

  • Content recommendation systems for personalized movie, music, or content suggestions based on user preferences and viewing history.
  • Sentiment analysis of social media conversations and reviews to understand audience reactions and engagement.
  • Text summarization for generating concise news summaries or show recaps.

Healthcare and Life Sciences:

  • Clinical text analysis for extracting insights from electronic health records (EHRs) and medical literature.
  • Medical image analysis to assist in diagnoses and treatment planning.
  • Drug discovery and pharmacovigilance using NLP to analyze biomedical literature and identify adverse drug reactions.

Finance and Banking:

  • Textual analysis of financial news, reports, and social media to assess market sentiment and make informed investment decisions.
  • Fraud detection and risk assessment by analyzing transactional data and detecting anomalies or suspicious patterns.
  • Chatbots and virtual assistants for customer inquiries, account management, and financial advice.

Travel and Hospitality:

  • Sentiment analysis of customer reviews and feedback to improve service quality and reputation management.
  • Language translation services to facilitate communication with international travelers.
  • Chatbots and virtual assistants for travel recommendations, itinerary planning, and customer support.
Future-proof your Enterprise and Innovate with Ampere Cloud Native Processors

Ampere Cloud Native Processors redefine the future of data center infrastructure, offering unrivaled capabilities to meet the demands of emerging technologies like AI, machine learning, and edge computing. With Ampere processors, businesses gain a competitive advantage by harnessing the power of cutting-edge advancements. Ampere processors provide the best energy efficiency on the market, addressing the pressing need to reduce data center energy consumption.

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.

©Ampere Computing. All Rights Reserved. Ampere, Ampere Computing, Altra and the ‘A’ logo are all registered trademarks or trademarks of Ampere Computing. Arm is a registered trademark of Arm Limited (or its subsidiaries). All other product names used in this publication are for identification purposes only and may be trademarks of their respective companies.

Created At : August 16th 2023, 10:14:27 am
Last Updated At : February 14th 2024, 5:45:18 pm
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