Nvidia´s Push for a 100-Fold Increase in AI Computing Power

Nvidia´s CEO advocates for a significant expansion in Artificial Intelligence computing capacity.

Nvidia´s CEO, Jensen Huang, underscored the urgent necessity for a dramatic increase in Artificial Intelligence computing power during a recent announcement. He emphasized that the demand for AI technologies is soaring, necessitating a 100-fold boost in computing abilities to meet global needs. This surge in capacity is crucial not only for advancing AI applications but also for maintaining competitive edge in the rapidly evolving tech landscape.

In his address, Huang introduced the Rubin AI chips, which are designed to significantly enhance processing capabilities. These state-of-the-art chips reflect Nvidia’s commitment to pushing the boundaries of AI technology and to supporting industries in leveraging AI for innovative solutions. The announcement has received considerable attention, suggesting optimism among stakeholders concerning Nvidia´s strategic direction.

Further strengthening Nvidia´s position, the company has forged new corporate partnerships aimed at expanding its influence in the tech industry. Such collaborations are expected to foster innovation and provide robust frameworks for integrating AI solutions across various sectors, thereby contributing to the overall ecosystem´s growth and stability.

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Samsung to supply half of NVIDIA’s SOCAMM2 modules in 2026

Hankyng reports Samsung Electronics has secured a deal to supply half of NVIDIA’s SOCAMM2 modules in 2026 for the Vera Rubin Superchip, which pairs two ‘Rubin’ Artificial Intelligence GPUs with one ‘Vera’ CPU and moves from hardwired memory to DDR5 SOCAMM2 modules.

NVIDIA announces CUDA Tile in CUDA 13.1

CUDA 13.1 introduces CUDA Tile, a virtual instruction set for tile-based parallel programming that raises the programming abstraction above SIMT and abstracts tensor cores to support current and future tensor core architectures. The change targets workloads including Artificial Intelligence where tensors are a fundamental data type.

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