NVIDIA is advancing AI infrastructure not just through raw power but by integrating hardware and software for greater efficiency and scalability. Their NVFP4 training format, hardware-software co-desi...
Marvell Technology’s Q1 earnings reveal growing demand for AI-specific chips, signaling a shift from GPU dominance to a more diverse AI hardware ecosystem. This trend highlights the increasing role of...
NVIDIA’s recent developer blog reveals key AI innovations: the NVFP4 low-precision training format, optimizations for long-context model training, and smart inference agents for gaming. These updates ...
Marvell Technology’s recent strong Q1 results highlight a growing trend in AI chip design: hardware specialization. Unlike competitors who lean on software optimization, Marvell focuses on custom sili...
NVIDIA’s recent developer updates reveal a clear focus on boosting AI training speed, cutting inference costs, and enhancing security through sandboxing agentic workflows. Together, these moves highli...
AI data centers are rapidly expanding, putting new pressures on regional power grids. Innovations like Alfa Laval’s FreeWaterLoop cooling and smarter grid management could help balance growth with sus...
AI data centers are driving unprecedented energy demand that’s straining the U.S. power grid. While grid modernization lags, innovations like liquid cooling, edge GPUs, and virtual power plants offer ...
Seven major AI hyperscalers have pledged—facilitated by the White House—to fully fund new power generation and grid upgrades to meet AI’s growing energy demands. This move signals a shift toward corpo...
The AI industry’s capital spending is set to more than double in 2026, signaling a new infrastructure supercycle. This surge reflects deep changes in hardware, networking, and power systems to meet AI...
NVIDIA’s NVFP4 low-precision format is driving significant efficiency gains in AI training and inference. From speeding up long-context training in JAX/XLA to boosting sovereign AI inference and cutti...