“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 ...

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 ...

The shift from training-focused to inference-focused economics is fundamentally restructuring cloud computing and forcing ...

An analog in-memory compute chip claims to solve the power/performance conundrum facing artificial intelligence (AI) inference applications by facilitating energy efficiency and cost reductions ...

AMD is strategically positioned to dominate the rapidly growing AI inference market, which could be 10x larger than training by 2030. The MI300X's memory advantage and ROCm's ecosystem progress make ...

Microsoft’s new Maia 200 inference accelerator chip enters this overheated market with a new chip that aims to cut the price ...

As AI applications increasingly permeate enterprise operations, from enhancing patient care through advanced medical imaging to powering complex fraud detection models and even aiding wildlife ...

Huawei has officially launched its new AI inference framework, Unified Cache Manager (UCM), following earlier reports about the company’s plans to reduce reliance on high-bandwidth memory (HBM) chips.

Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed ...

Micron Technology is poised for explosive growth, driven by surging AI demand and its dominant position in high-bandwidth memory for leading GPUs. MU's HBM products are sold out through 2025, with ...