With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...

A new technical paper titled “Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review” was published in “Proceedings of the IEEE” by researchers at University ...

Large language models (LLMs) have made significant strides in artificial intelligence (AI) natural language generation. Models such as GPT-3, Megatron-Turing, Chinchilla, PaLM-2, Falcon, and Llama 2 ...

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

Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability, particularly for reasoning and planning (known as System 2 abilities) has been lacking.

Researchers from DeepSeek and Tsinghua University say combining two techniques improves the answers the large language model creates with computer reasoning techniques. Image: Envato/DC_Studio ...

Cluster-robust inference and estimation methods have emerged as indispensable tools in empirical research, enabling statisticians and economists to draw valid conclusions from data exhibiting ...

NORMAN, Okla. – Song Fang, a researcher with the University of Oklahoma, has been awarded funding from the U.S. National Science Foundation to create training-free detection methods and novel ...