TTT-Discover optimizes GPU kernels 2x faster than human experts — by training during inference

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

The $20 Billion Bet On Inference: What Every AI Infrastructure Team Needs To Get Right

Every ChatGPT query, every AI agent action, every generated video is based on inference. Training a model is a one-time ...
WinBuzzer

AI: Memory Bottleneck Emerges as Main LLM Inference Challenge

Google researchers have revealed that memory and interconnect are the primary bottlenecks for LLM inference, not compute power, as memory bandwidth lags 4.7x behind.
VentureBeat

When AI reasoning goes wrong: Microsoft Research shows more tokens can mean more problems

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Large language models (LLMs) are ...
Nature

Bayesian Inference and Statistical Inverse Problems

Bayesian inference provides a robust framework for combining prior knowledge with new evidence to update beliefs about uncertain quantities. In the context of statistical inverse problems, this ...
TheStreet.com

Inference Isn’t A Problem. To Democratize AI, We Need To Cut The Costs Of Data Access

“The rapid release cycle in the AI industry has accelerated to the point where barely a day goes past without a new LLM being announced. But the same cannot be said for the underlying data,” notes ...
Electronic Design

Three Tips for Boosting CNN Inference Performance

How to improve the performance of CNN architectures for inference tasks. How to reduce computing, memory, and bandwidth requirements of next-generation inferencing applications. This article presents ...