Accelerated computing goes beyond just speed—it’s about making every application run more efficiently. From improving your video calls to helping researchers run simulations using 140X less energy, NVIDIA’s CUDA GPU-accelerated computing saves energy and cuts costs. Read more on our blog: https://nvda.ws/4cJeKeS #SustainableComputing #CUDA #AI
8/28!!!!
The innovations in CUDA-accelerated computing are truly impressive, especially the dramatic speedups and energy efficiency gains in AI workloads. Given the rapid growth of data and the increasing demand for real-time AI interactions, how do you see the integration of your new libraries like cuVS and NeMo Curator impacting the scalability of AI-driven solutions in cloud-based environments? Are there any best practices for businesses looking to maximize these benefits in their AI applications?
Accelerated computing is indeed the future of sustainable computing! NVIDIA's vision for a more efficient and cost-effective digital ecosystem is inspiring. The impact of CUDA GPU-accelerated computing on various industries, from scientific research to e-commerce, is remarkable. I'm excited to see how NVIDIA's continued innovation will shape the future of computing and help address some of the world's most pressing challenges. Congratulations on your commitment to sustainable computing!
I'm excited for the 5000 series GeForce RTX GPUs!
This impressive speedup of various applications when running on GPU compared to CPU, showcasing NVIDIA's CUDA GPU-accelerated computing. It's remarkable to see how these advancements aren't just about faster processing times but also about energy efficiency. With up to 180X speedups in tasks like feature engineering and data processing, it's clear that GPUs are playing a pivotal role in transforming computational tasks across industries. This kind of performance leap not only reduces operational costs but also paves the way for more sustainable computing practices. The future of high-performance computing is here, and it's greener and more efficient than ever.
Customers migrating from CPUs to NVIDIA AI in the cloud are realizing incredible workload speedups. We provide CUDA libraries across use-cases to drive rapid adoption for every business.
Also the water cooled chips should make it easier for energy recovery systems coupled to data centers. Instead of rejecting all of the heat, it would be captured and utilized for other industrial processes.
This is a win win for both businesses and the environment.
As performance engineering experts we are really excited about this.