Engineering & AI Teams

Computers designed for AI, machine learning, and simulation workloads are equipped with high-core-count CPUs, such as Intel Xeon W-series (up to 36 cores) or AMD Ryzen Threadripper Pro (up to 64 cores), providing exceptional performance for simulation, data analysis, code compilation, and multi-threaded tasks. These systems include high-performance NVIDIA GPUs, such as RTX 4090, A6000, or A100 series, featuring thousands of CUDA cores for accelerated AI training, deep learning, neural network computation, and GPU-intensive machine learning workloads.

To maintain stability during long, computation-heavy tasks, workstations are equipped with large-capacity ECC RAM, typically 64GB to 512GB DDR4/DDR5, which detects and corrects memory errors to ensure reliable processing of massive datasets. For storage, they utilize ultra-fast PCIe Gen4/Gen5 NVMe SSDs ranging from 2TB to 8TB, delivering rapid read/write speeds of 7,000–12,000 MB/s for quick access to datasets and project files. Additional enterprise-grade SSDs or HDDs are often included for archival storage and large-scale dataset management.

These workstations are highly upgradeable, allowing users to increase RAM capacity, add additional NVMe or SATA storage, or upgrade GPUs as AI models and computational demands grow. Many motherboards support multi-GPU setups, liquid cooling, and high-power PSU options, making these systems scalable, reliable, and future-proof solutions for research, AI development, and simulation-intensive workflows.