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.

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 ...

Detailed in a recently published technical paper, the Chinese startup’s Engram concept offloads static knowledge (simple ...

From finance to SaaS, different LLMs win different jobs, which is why a single-platform AI strategy won’t work in 2026.

A new technical paper titled “Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference” was published by researchers at University of Cambridge, Imperial College London ...

A new technical paper titled “System-performance and cost modeling of Large Language Model training and inference” was published by researchers at imec. “Large language models (LLMs), based on ...

One-bit large language models (LLMs) have emerged as a promising approach to making generative AI more accessible and affordable. By representing model weights with a very limited number of bits, ...

ByteDance's Doubao AI team has open-sourced COMET, a Mixture of Experts (MoE) optimization framework that improves large language model (LLM) training efficiency while reducing costs. Already ...

Nvidia did not submit results for Blackwell either, as it wasn’t ready when results had to be submitted, but still won the race with the Hopper GPU by up to 4X. Too bad for AMD, as they probably have ...

Demand for AI solutions is rising—and with it, the need for edge AI is growing as well, emerging as a key focus in applied machine learning. The launch of LLM on NVIDIA Jetson has become a true ...