Here is how the prefill versus generation split exposes GPU structural inefficiencies in AI processor designs.

Developers and system architects today face a growing demand to enable large language model variants on device. They are facing pressure to support transformer-capable models on constrained devices to ...

My self-hosted setup holds up pretty well for my coding tasks ...

Imagine working at a warehouse or office sometime in the near future, and you're asked to help a new trainee learn the basics ...

Aaron Erickson discusses the evolution of AI workflows, shifting from "vibe checking" to building reliable, multi-agent ...

Opinion: We don't yet know AI's upper limits, so it's important to give law students a meaningful AI education. This should ...

Abstract: The mainstreamTransformer-based Large Language Models (LLMs) have demonstrated to exhibit remarkable performance in various Natural Language Processing (NLP) tasks. However, high ...

We tested both on writing, coding, research, and video. See which one fits your workflow, budget, and use case.

As vision-centric large language models move on-device, performance measured in raw TOPS is no longer enough. Architectures need to be built around real workloads, memory behavior, and sustained ...

Google on Monday disclosed that it identified an unknown threat actor using a zero-day exploit that it said was likely developed with an artificial intelligence (AI) system, marking the first time the ...

The rapid ascent of large language models (LLMs)—and their growing role in everyday life—masks a fundamental problem: Generative Pre-trained Transformer (GPT) models hallucinate, struggle with ...