The biggest challenge posed by AI training is in moving the massive datasets between the memory and processor.
As agentic AI moves from experiments to real production workloads, a quiet but serious infrastructure problem is coming into focus: memory. Not compute. Not models. Memory.
The growing imbalance between the amount of data that needs to be processed to train large language models (LLMs) and the inability to move that data back and forth fast enough between memories and ...
Researchers propose low-latency topologies and processing-in-network as memory and interconnect bottlenecks threaten inference economic viability ...
Micron, SK Hynix and Samsung Electronics — make up nearly the entire RAM market, and they're benefitting from this shortage.
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Data centers will consume 70 percent of memory chips made in 2026 - supply shortfall will cause the chip shortage to spread to other segments
The shortages are driven by explosive AI demand, and the latest report says that up to 70 percent of the memory produced worldwide in 2026 will be consumed by data centers. However, those specific ...
Researchers have created a new kind of 3D computer chip that stacks memory and computing elements vertically, dramatically speeding up how data moves inside the chip. Unlike traditional flat designs, ...
As AI models and computing demands continue to grow exponentially, the biggest challenge in chip design is no longer pure processing power, but the bandwidth gap between processors and memory. Even ...
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