Recently, the team led by Guoqi Li and Bo Xu from the Institute of Automation, Chinese Academy of Sciences, published a ...

Multimodal large language models have shown powerful abilities to understand and reason across text and images, but their ...

A Nature paper describes an innovative analog in-memory computing (IMC) architecture tailored for the attention mechanism in large language models (LLMs). They want to drastically reduce latency and ...

One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them. In the context of AI, quantization refers to ...

Researchers have introduced a technique for compressing a large language model's reams of data, which could increase privacy, save energy and lower costs. The new algorithm works by trimming ...

SwiftKV optimizations developed and integrated into vLLM can improve LLM inference throughput by up to 50%, the company said. Cloud-based data warehouse company Snowflake has open-sourced a new ...

The rapid advancements in AI have brought powerful large language models (LLMs) to the forefront. However, most high-performing models are massive, compute-heavy, and require cloud-based inference, ...

“Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the ...

A Chinese AI company's more frugal approach to training large language models could point toward a less energy-intensive—and more climate-friendly—future for AI, according to some energy analysts. "It ...

As tech companies race to deliver on-device AI, we are seeing a growing body of research and techniques for creating small language models (SLMs) that can run on resource-constrained devices. The ...