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

Researchers propose low-latency topologies and processing-in-network as memory and interconnect bottlenecks threaten inference economic viability ...

DeepSeek’s conditional memory fixes silent LLM waste: GPU cycles lost to static lookups

Through systematic experiments DeepSeek found the optimal balance between computation and memory with 75% of sparse model ...
EDN

AI’s insatiable appetite for memory

The biggest challenge posed by AI training is in moving the massive datasets between the memory and processor.
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 ...
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 ...
VentureBeat

New LLM optimization technique slashes memory costs up to 75%

Researchers at the Tokyo-based startup Sakana AI have developed a new technique that enables language models to use memory more efficiently, helping enterprises cut the costs of building applications ...
InfoWorld

Why LLM applications need better memory management

Generative AI applications don’t need bigger memory, but smarter forgetting. When building LLM apps, start by shaping working memory. You delete a dependency. ChatGPT acknowledges it. Five responses ...