By submitting this form, you are agreeing to the Terms of Use and Privacy Policy.
In order to handle sophisticated mathematical and graphical tasks, dedicated servers with GPUs are equipped with one or more dedicated graphics processing units. GPUs are an excellent choice for applications that call for the concurrent processing of huge datasets since they can carry out some calculations significantly faster than conventional CPUs.
These servers are frequently used in fields that need high-performance processing, such as video and photo editing, machine learning, gaming, and scientific research. Because they can speed up neural network training and inference, GPU-based servers are a crucial tool in machine learning and deep learning.
Numerous algorithms rely heavily on neural networks, which need a lot of processing power to train on massive datasets. Data scientists and machine learning engineers can train and run models considerably more quickly than they could with a CPU alone by using a dedicated server with graphics cards.
As a result, machine learning model performance and accuracy may significantly improve, and model development and deployment may proceed more quickly. Anyone working in the field of artificial intelligence needs a GPU server because it makes it possible to train and execute more complicated models and process larger datasets more rapidly and effectively.
The Global GPU server market accounted for $XX Billion in 2022 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2023 to 2030.
In order to assist developers quickly create customized, AI-powered applications that can provide new services and insights, NVIDIA launched four inference platforms that are designed for a variety of quickly emerging generative AI applications.
The systems integrate the most recent NVIDIA Ada, NVIDIA Hopper, and NVIDIA Grace Hopper processors, including the NVIDIA L4 Tensor Core GPU and the NVIDIA H100 NVL GPU, both introduced today, with NVIDIA’s full stack of inference software.
For in-demand workloads like AI video, image production, massive language model deployment, and recommender inference, each platform is prepared.
When compared to CPUs, NVIDIA L4 for AI Video can deliver 120x higher AI-powered video performance while using 99% less energy.It provides improved video decoding and transcoding capabilities, video streaming, augmented reality, generative AI video, and more, acting as a universal GPU for practically any job.
