AI Infrastructure Evolution: How Better Hardware Powers The LLM Era

Running both phases on the same silicon creates inefficiencies, which is why decoupling the two opens the door to new ...
WinBuzzer

AI: Memory Bottleneck Emerges as Main LLM Inference Challenge

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.
SDxCentral

DeepSeek looks to offload simple LLM tasks to save billions of parameters

Detailed in a recently published technical paper, the Chinese startup’s Engram concept offloads static knowledge (simple ...
Semiconductor Engineering

Pooling CPU Memory for LLM Inference With Lower Latency and Higher Throughput (UC Berkeley)

“The rapid growth of LLMs has revolutionized natural language processing and AI analysis, but their increasing size and memory demands present significant challenges. A common solution is to spill ...
Business Wire

Kioxia Achieves Successful Prototyping of 5TB Large-Capacity and 64GB/s High-Bandwidth Flash Memory Module

TOKYO--(BUSINESS WIRE)--Kioxia Corporation, a world leader in memory solutions, has successfully developed a prototype of a large-capacity, high-bandwidth flash memory module essential for large-scale ...
Semiconductor Engineering

HW-based Heterogeneous Memory Management for LLM Inferencing (KAIST, Stanford Unversity)

A new technical paper titled “Hardware-based Heterogeneous Memory Management for Large Language Model Inference” was published by researchers at KAIST and Stanford University. “A large language model ...