Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run
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Category: Misc
Level up GeForce NOW experiences this summer with 40% off Performance Day Passes. Enjoy 24 hours of premium cloud gaming with RTX ON, delivering low latency and shorter wait times. The hot deal comes just in time for the cloud’s highly anticipated launch of Dune: Awakening — a multiplayer survival game on a massive scale
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NVIDIA TensorRT for RTX is now available for download as an SDK that can be integrated into C++ and Python applications for both Windows and Linux. At…
NVIDIA TensorRT for RTX is now available for download as an SDK that can be integrated into C++ and Python applications for both Windows and Linux. At Microsoft Build, we unveiled this streamlined solution for high-performance AI inference that supports NVIDIA GeForce RTX GPUs from NVIDIA Turing through NVIDIA Blackwell generations, including the latest NVIDIA RTX PRO lineup.
In the rapidly evolving robotics and edge AI landscape, the ability to efficiently process and transfer sensor data is crucial. Many edge applications are…
In the rapidly evolving robotics and edge AI landscape, the ability to efficiently process and transfer sensor data is crucial. Many edge applications are moving away from single-sensor fixed-function solutions and in favor of diverse sensor arrays. These include vision, audio, temperature, force/torque, and communication sensors, IMUs, idaradar systems, ultrasounds, motors, and actuators.
SANTA CLARA, Calif., June 11, 2025 (GLOBE NEWSWIRE) — NVIDIA today announced it will hold its 2025 Annual Meeting of Stockholders online on Wednesday, June 25, at 9 a.m. PT. The meeting will …
Biomedical research and drug discovery have long been constrained by labor-intensive processes. In order to kick-off a drug discovery campaign, researchers…
Biomedical research and drug discovery have long been constrained by labor-intensive processes. In order to kick-off a drug discovery campaign, researchers typically comb through numerous scientific papers for details about known protein targets and small molecule pairs. Reading—and deeply comprehending—a single paper takes one to six hours, while summarizing findings without AI assistance…
The emergence of models like AlphaFold2 has skyrocketed the demand for faster inference and training of molecular AI models. The need for speed comes with…
The emergence of models like AlphaFold2 has skyrocketed the demand for faster inference and training of molecular AI models. The need for speed comes with unique computational challenges, including algorithmic complexity, memory efficiency, and strict accuracy requirements. To address this, NVIDIA collaborated with partners to provide accelerated solutions like faster equivariant operations and…
Building smarter robots and autonomous vehicles (AVs) starts with physical AI models that understand real-world dynamics. These models serve two critical roles:…
Building smarter robots and autonomous vehicles (AVs) starts with physical AI models that understand real-world dynamics. These models serve two critical roles: accelerating synthetic data generation (SDG) to help autonomous machines learn about real-world physics and interactions—including rare edge cases—and serving as base models that can be post-trained for specialized tasks or adapted to…
Physical AI enables autonomous systems—think robots, self-driving cars, and smart spaces—to perceive, understand, and act intelligently in the real world….
Physical AI enables autonomous systems—think robots, self-driving cars, and smart spaces—to perceive, understand, and act intelligently in the real world. However, effectively training these complex systems takes immense and diverse datasets. Relying solely on real-world data collection is often costly, time-consuming, and limited by safety and practical constraints. To overcome this…
Telecom companies last year spent nearly $295 billion in capital expenditures and over $1 trillion in operating expenditures. These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations. For example, telcos must constantly tune network parameters for tasks — such as transferring calls from
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