Deploying large language models can be slow and costly, but smart optimization changes that. From GPU memory tricks to hybrid CUDA graph execution, new methods are slashing latency and boosting ...

Git isn't hard to learn, and when you combine Git and GitHub, you've just made the learning process significantly easier. This two-hour Git and GitHub video tutorial shows you how to get started with ...

Abstract: Distributed optimization provides a framework for deriving distributed algorithms for a variety of multi-robot problems. This tutorial constitutes the first part of a two-part series on ...

When Nvidia first showed off its Compute Unified Device Architecture (CUDA) parallel computing platform in 2006, it was a multibillion-dollar bet that failed to turn a profit for a decade. Today, it ...

I am a hacker, engineer, product manager, and researcher on LLMs, AI/ML, and the ethics of applied machine learning. I am a hacker, engineer, product manager, and researcher on LLMs, AI/ML, and the ...

../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [63,0,0] Assertion -sizes[i] <= index && index < sizes[i] && "index out of bounds ...

The CUDA toolkit is now packaged with Rocky Linux, SUSE Linux, and Ubuntu. This will make life easier for AI developers on these Linux distros. It will also speed up AI development and deployments on ...

At the heart of CUDA-L1 lies a major leap in AI learning strategy: Contrastive Reinforcement Learning (Contrastive-RL). Unlike traditional RL, where an AI simply generates solutions, receives ...

Olivera Ciraj Bjelac, IAEA Department of Nuclear Sciences and Applications To support hospitals and specialists around the world in meeting their safety standards requirements, the IAEA has produced a ...

Apple’s MLX machine learning framework, originally designed for Apple Silicon, is getting a CUDA backend, which is a pretty big deal. Here’s why. The work is being led by developer @zcbenz on GitHub ...