Qualcomm and AMD are exploring SOCAMM2 memory for AI hardware, following NVIDIA’s lead as demand for faster, modular memory grows.
Kioxia Corporation announced that it has begun sampling new UFS Ver. 4.1 embedded memory devices with QLC technology.
Google researchers have revealed that memory and interconnect are the primary bottlenecks for LLM inference, not compute power, as memory bandwidth lags 4.7x behind.
Overview: AI data centers consume massive amounts of high-performance memory, shifting demand away from consumer markets.Enterprise-scale AI deployments are tig ...
Qualcomm’s AI200 and AI250 move beyond GPU-style training hardware to optimize for inference workloads, offering 10X higher memory bandwidth and reduced energy use. It’s becoming increasingly clear ...
Nvidia CEO Jensen Huang recently declared that artificial intelligence (AI) is in its third wave, moving from perception and generation to reasoning. With the rise of agentic AI, now powered by ...
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