NVIDIA's NVFP4 low-precision format significantly enhances AI model training and inference by boosting throughput and energy efficiency without sacrificing accuracy. This analysis explores NVFP4's tec...
NVIDIA’s NVFP4 low-precision training and support for long-context models, combined with OpenAI and Oracle’s Project Stargate, are driving a new era in AI infrastructure. This shift emphasizes integra...
The Mesh argues that rising CPU demand driven by agentic AI requires a strategic shift in AI infrastructure priorities. While GPUs remain vital, CPUs now play an indispensable role in managing complex...
Marvell Technology forecasted strong fiscal 2028 revenue growth driven by demand for its AI-focused chips and interconnect technologies, following a first-quarter earnings beat. The company’s outlook ...
The Mesh argues that for edge AI hardware, energy efficiency has become more critical than minimizing chip size due to stringent power constraints in real-world devices. Prioritizing power consumption...
Recent advances in AI chip security, optical interconnects, and ultra-high bandwidth memory are driving a fundamental shift in semiconductor design. These innovations address emerging AI integrity thr...
Unified AI infrastructure at the edge integrates compute, connectivity, and power management within micro data centers, enabling real-time AI workloads with ultra-low latency and improved power effici...
Marvell Technology’s recent earnings and growth forecast highlight a shift in AI data center infrastructure toward specialized chips and interconnects. This trend reflects broader industry moves towar...
The rapid growth of AI workloads is intensifying strain on US power grids, prompting a shift from centralized hyperscale data centers to distributed edge and micro data centers. Innovations in power-e...
Marvell Technology’s recent earnings beat, driven by demand for AI-focused interconnect chips, signals a growing market recognition of the critical role that network infrastructure plays alongside GPU...