InfoWorld

The ‘toggle-away’ efficiencies: Cutting AI costs inside the training loop

You don't need the newest GPUs to save money on AI; simple tweaks like "smoke tests" and fixing data bottlenecks can slash ...
IEEE

Efficient KV Cache Spillover Management on Memory-Constrained GPU for LLM Inference

Abstract: The rapid growth of model parameters presents a significant challenge when deploying large generative models on GPU. Existing LLM runtime memory management solutions tend to maximize batch ...
IEEE

Effective Memory Management and Sparse Aware Cache for Row-Wise Sparse CNNs

Abstract: Convolutional Neural Networks (CNNs) are used in several image processing tasks like image recognition and object localization. For edge applications such as drones and autonomous vehicles, ...
VentureBeat

New KV cache compaction technique cuts LLM memory 50x without accuracy loss

Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working ...
EDN

Last-level cache has become a critical SoC design element

As AI workloads extend across nearly every technology sector, systems must move more data, use memory more efficiently, and respond more predictably than traditional design methodologies allow. These ...
PC Magazine

RAM Reality Check: How Much Memory Does Your PC Actually Need in 2026?

The AI hardware boom is sending memory prices sky-high, so knowing exactly how much you need is more critical than ever. I've worked out the most realistic RAM goals for every type of PC. I’ve been a ...