AI Development, Local LLM, AI Workstation for DL
AI Development, Local LLM, Deep Learning AI Workstation
The Applied AI Workstation refers to a high-performance computer optimized for advanced AI development tasks that require vast computational resources, such as deep learning, machine learning, data analysis, and simulation. By equipping the latest generation CPUs, large-capacity memory, high-speed storage, and high-end GPUs, it enables fast and stable execution of training and inference processes for large-scale neural networks. Additionally, with a software environment and development tools optimized for Linux-based operating systems like Ubuntu, it provides flexibility and scalability that allows researchers and developers to start working immediately. It can accommodate a wide range of industries and applications, including not only AI development but also scientific computing, image processing, and CAD/CAE analysis. Hardware configuration for AI development, local LLM, and deep learning workstation.
basic information
Recommended models for AI development, local LLM, and deep learning applications: Core Ultra processors, high-end GPUs like RTX 5090/RTX PRO 6000, and models equipped with Linux Ubuntu.
Price range
Delivery Time
Applications/Examples of results
Main Needs Training large-scale AI models Parallel processing with multiple GPUs allows for the rapid training of complex deep learning models such as Transformers and CNNs. High-speed processing of large datasets In AI/statistical analysis dealing with tens of millions to hundreds of millions of data points, high memory bandwidth and storage I/O speed are required. Consistent development from research/prototyping to practical application There is a need to handle everything from experimental algorithm validation to integration into products and proof of concept (PoC) with a single machine. Workflow efficiency and cost reduction By avoiding reliance on the cloud and establishing an on-premises computing environment, communication delays and running costs can be reduced.
