AI inferencing feels the need

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    Commissioned Speed and performance often play an outsized role in determining outcomes of many competitions. The famed Schneider Trophy races of the early 20th century offer a classic example, as multiple nations pushed the boundaries of speed in feats of aerial supremacy.

    Italy and United States produced strong showings, but it was Britain’s Supermarine S.6B seaplane that secured victory in the final race, setting a then world speed record of over 400 miles per hour. Quaint by today’s standards, with jet fighters topping Mach 3, but a marvel for its time.

    Like the famed Schneider Trophy races, the scramble for AI supremacy is a competition where high speed and performance are critical.

    This is particularly salient for generative AI, the emerging class of technologies that use large language models to process anything from text to audio and images. Also like its AI predecessors, generative AI relies on high-quality training data and its next phase, known as inferencing.

    Why inference matters for predictions

    AI inferencing works like this: After a machine learning model is trained to recognize the patterns and relationships in a large amount of labeled data, the model takes new data as input and applies the learned knowledge from the training phase to generate predictions or perform other tasks. Depending on the model (or models), the input data could include text, images or even numerical values.

    As input data flows through the model’s computational network, the model applies mathematical operations. The final output of the model represents the inference, or prediction, based on the input.

    Ultimately, it takes a combination of the trained model and new inputs working in near real-time to make quick decisions or predictions for such critical tasks as natural language processing, image recognition or recommendation engines.

    Consider recommendation engines. As people consume content on ecommerce or streaming platforms, the AI models track the interactions, “learning” what people prefer to purchase or watch. The engines use this information to recommend content based on the preference history.

    Using generative AI models, businesses can analyze purchase history, browsing behavior and other signals to personalize messages, offers and promotions to individual customers. Nearly a third of outbound marketing messages enterprises send will be fueled by AI, according to Gartner.

    To ensure that these engines serve up relevant recommendations, processing speed is essential. Accordingly, organizations leverage various optimizations and hardware acceleration to facilitate the inference process.

    Why Generative AI needs speedy hardware

    Generative AI is a computation-hungry beast. As it trains on massive data sets to learn patterns, it requires significant processing firepower and storage, as well as validated design blueprints to help right-size configurations and deployments.

    Emerging classes of servers come equipped with multiple processors or GPUs to accommodate modern parallel processing techniques, in which workloads are split across multiple cores or devices to speed up training and inference tasks.

    And as organizations add more parameters – think millions or possibly billions of configuration variables – they often must add more systems to process the input data and crunch calculations. To accommodate these larger data sets, organizations often interconnect multiple servers, creating scalable infrastructure. This helps ensure that AI training and inferencing can maintain performance while handling growing requirements.

    Ultimately, powerful servers and reliable storage are critical as they facilitate faster and more accurate training, as well as real-time or near-real-time inferencing. Such solutions can help organizations tap into the potential of generative AI for various applications.

    Speedy algorithms will win the day

    There’s no question the Schneider Trophy aerial races of last century left their mark on the history of aviation. And just as those multinational races underscore how competition can fuel surprising advancements in speed and engineering, the AI arms race highlights the importance of technological innovation driving today’s businesses.

    Organizations that ride this new wave of AI will realize a competitive advantage as they empower developers with the tools to build smarter applications that deliver material business outcomes.

    As an IT leader you should arm your department with the best performing inferencing models along with the hardware to fuel them. May the best generative AI algorithm(s), and models, win.

    Learn more about Dell Technologies APEX is fueling this new era of AI inferencing.

    Commissioned by Dell Technologies

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    Виктор Попанов
    Эксперт тестовой лаборатории. Первый джойстик держал в руках в возрасте 3 лет. Первый компьютер, на котором „работал” был с процессором Intel i386DX-266. Тестирует оборудование для издания ITBusiness. Будь то анализ новейших гаджетов или устранение сложных неполадок, этот автор всегда готов к выполнению поставленной задачи. Его страсть к технологиям и приверженность качеству делают его бесценным помощником в любой команде.