Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

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 ...
VentureBeat

How attention offloading reduces the costs of LLM inference at scale

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Rearranging the computations and hardware used to serve large language ...
NextBigFuture

Defeating Nondeterminism in LLM Inference by Thinking Machines

A research article by Horace He and the Thinking Machines Lab (X-OpenAI CTO Mira Murati founded) addresses a long-standing issue in large language models (LLMs). Even with greedy decoding bu setting ...
InfoWorld

Multi-token prediction technique triples LLM inference speed without auxiliary draft models

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.
Semiconductor Engineering

LLM Inference on GPUs (Intel)

“Transformer based Large Language Models (LLMs) have been widely used in many fields, and the efficiency of LLM inference becomes hot topic in real applications. However, LLMs are usually ...
TechRepublic

NVIDIA Boosts LLM Inference Performance With New TensorRT-LLM Software Library

NVIDIA Boosts LLM Inference Performance With New TensorRT-LLM Software Library Your email has been sent As companies like d-Matrix squeeze into the lucrative artificial intelligence market with ...
Forbes

The New Frontier Of LLM Inference: Where The Next Tenfold Gains Will Come From

Shakti P. Singh, Principal Engineer at Intuit and former OCI model inference lead, specializing in scalable AI systems and LLM inference. Generative models are rapidly making inroads into enterprise ...