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 ...
“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 ...
The shift from training-focused to inference-focused economics is fundamentally restructuring cloud computing and forcing ...
FriendliAI also offers a unique take on the current memory crisis hitting the industry, especially as inference becomes the dominant AI use case. As recently explored by SDxCentral, 2026 is tipped to ...
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 ...
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 ...
Nvidia agreed to acquire Groq's AI inference chip assets for $20b, aiming to expand its position in AI deployment hardware. The company introduced its new Rubin chip platform, designed around next ...
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 ...
NVIDIA (NVDA) BlueField-4 powers NVIDIA Inference Context Memory Storage Platform, a new kind of AI-native storage infrastructure designed for gigascale inference, to accelerate and scale agentic AI.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results