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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 ...
SDxCentral

AI inference crisis: Google engineers on why network latency and memory trump compute

Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...
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How AI chip upstart FuriosaAI won over LG with its power-sipping design

South Korean AI chip startup FuriosaAI scored a major customer win this week after LG's AI Research division tapped its AI accelerators to power servers running its Exaone family of large language ...
Semiconductor Engineering

Dynamic KV Cache Scheduling in Heterogeneous Memory Systems for LLM Inference (Rensselaer Polytechnic Institute, IBM)

A new technical paper titled “Accelerating LLM Inference via Dynamic KV Cache Placement in Heterogeneous Memory System” was published by researchers at Rensselaer Polytechnic Institute and IBM. “Large ...
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 ...