Experiment, Prototype, and Innovate with AI: NVIDIA RTX-Powered AI Workstations
NVIDIA RTX AI workstations offer incredible desktop computing performance — perfectly suited for AI training, inference and data science workflows. This solution brief gives you an overview of workflows managed, main features and benefits, and recommended configurations. Download your copy of the solution brief for the details you need to make an informed decision about the NVIDIA RTX-powered workstation that best meets your AI development needs.
What are NVIDIA RTX-Powered AI Workstations?
NVIDIA RTX-powered AI workstations are high-performance desktop systems designed to facilitate AI training, inference, and data science tasks. They are equipped with up to four NVIDIA RTX 6000 Ada Generation GPUs, providing a combined AI compute performance of 5.8 petaflops and 192GB of total system GPU memory. These workstations are optimized for processing smaller AI models locally while seamlessly integrating with data center and cloud resources for larger, more complex models.
How do AI Workstations enhance productivity?
NVIDIA RTX-powered AI workstations enhance productivity by reducing latency, which allows for real-time data preprocessing, exploration, and evaluation of features and models. Their large GPU memory configurations enable users to run multiple applications simultaneously, maximizing efficiency. Additionally, these workstations support NVIDIA CUDA®-X libraries, including RAPIDS, which significantly boosts performance for data science tasks without requiring code modifications.
What challenges do businesses face with AI model training?
Businesses face several challenges when training AI models, including the increasing size of models that can take months to train, which strains data center and cloud resources. Additionally, off-the-shelf models often require fine-tuning with domain-specific data. NVIDIA RTX-powered AI workstations provide a robust and cost-effective solution for AI research and development, allowing teams to train models on smaller datasets and optimize results while conserving cloud and data center resources.
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