Elith has introduced state-of-the-art GPUs (Graphics Processing Units)※. This significantly reduces AI model training and inference time, achieving acceleration in both AI development and real-world deployment.
■ Background: Truly driving the two wheels of “research x implementation”
Elith works across a wide range of AI domains, including healthcare, manufacturing, and operational optimization. In recent years, as the computational resources required for improving model accuracy have grown, we recognized the need for a more powerful development infrastructure.
With this background in mind, introducing GPUs has significantly reduced the processing time required for model training, inference, and evaluation. This is expected to shorten the lead time from PoC to production implementation, making the real-world deployment of research outcomes even smoother.
■ Key benefits of introducing GPUs
- Dramatically faster processing: Compresses the development cycle, enabling faster delivery of value
- Building high-precision models: Advances sophisticated model development that makes full use of large volumes of data
- Operational efficiency and cost optimization: Reduced time cuts the burden of development and operations
- Strengthened competitive advantage: Building the latest technology in-house improves responsiveness to the market
■ Looking ahead: Toward real-world deployment and product creation, under the motto “Accelerate!”
Elith's slogan, “Accelerate!,” is a guiding principle that runs through development, implementation, and social contribution alike. Introducing GPUs is merely a symbolic first step.
Going forward, we will make full use of this development infrastructure to accelerate the implementation of AI that addresses social challenges, as well as the creation of in-house products and new businesses.
※ What is a GPU (Graphics Processing Unit)?
A GPU is a specialized chip originally developed for high-speed processing of images and video. In recent years, its high parallel processing capability has also made it indispensable for computationally intensive tasks such as AI model training and inference.
With processing performance that overwhelmingly surpasses that of conventional CPUs (central processing units), it strongly supports the development of next-generation AI technologies such as generative AI and large language models (LLM).