Context parallelism (CP) for distributed inference and training for biomolecular folding models across multiple GPUs using a 2D CP mesh combined with data parallelism, demonstrated with the Boltz ...

LFM2.5-230M proves that while 3-billion-parameter models like VibeThinker are solving advanced calculus, a ...

When using parallel, please include the following: Vega Yon GG, Quistorff B. parallel: A command for parallel computing. The Stata Journal. 2019;19(3):667-684. doi:10 ...

Local AI inference at 32B-parameter quality, no cloud API required: University of Waterloo researchers released PAW on July 2 ...

Transaction Data Across Xsolla's Publisher Network Shows D2C On PC Operating At Scale Across More Than 1,000 Games, An ...

Abstract: In the past few years, deep learning (DL) techniques for predicting remaining useful life (RUL) have shown remarkable advancements, but model prediction accuracy and generalization to ...

Abstract: In parallel distributed data processing frameworks like Spark and Flink, task scheduling has a great impact on cluster performance. Though task Scheduling has proven to be an NP-complete ...

Google's open-source diffusion language model generates 256 tokens in parallel and self-corrects, hitting 4x speed on one GPU at a cost to quality.

NVIDIA diffusion language model Nemotron TwoTower achieves 2.42x LLM inference throughput without a full retraining run, ...

The teams that get full value from reality capture treat human change as the real implementation. They build the new workflow ...

Throwing money at massive GPUs won't fix your AI budget; you need to optimize your software and rethink your cloud strategy ...

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...