EDN

Round pegs, square holes: Why GPGPUs are an architectural mismatch for modern LLMs

The saying “round pegs do not fit square holes” persists because it captures a deep engineering reality: inefficiency most often arises not from flawed components, but from misalignment between a ...
IEEE

LLM Assistance for Memory Safety

Abstract: Memory safety violations in low-level code, written in languages like C, continues to remain one of the major sources of software vulnerabilities. One method of removing such violations by ...
blockchain

NVIDIA's Breakthrough in LLM Memory: Test-Time Training for Enhanced Context Learning

NVIDIA introduces a novel approach to LLM memory using Test-Time Training (TTT-E2E), offering efficient long-context processing with reduced latency and loss, paving the way for future AI advancements ...
Microsoft

Effects of LLM Use and Note-Taking On Reading Comprehension and Memory: A Randomised ...

Students’ rapid uptake of Generative Artificial Intelligence tools, particularly large language models (LLMs), raises urgent questions about their effects on learning. We compared the impact of LLM ...
IEEE

MI-LLM: Multiplier-free LLM Inference on Commodity Processing-in-Memory Hardware

Abstract: Large language models (LLMs) are prominent for their superior ability in language understanding and generation. However, a notorious problem for LLM inference is low computational ...
InfoWorld

Unlocking LLM superpowers: How PagedAttention helps the memory maze

Large language models (LLMs) like GPT and PaLM are transforming how we work and interact, powering everything from programming assistants to universal chatbots. But here’s the catch: running these ...
Semiconductor Engineering

Optimizing LLM Training Under GPU Memory Constraints (Argonne, RIT)

A new technical paper titled “MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall” was published by researchers at Argonne National Laboratory and ...
blockchain

Enhancing LLM Inference with CPU-GPU Memory Sharing

NVIDIA introduces a unified memory architecture to optimize large language model inference, addressing memory constraints and improving performance. Large Language Models (LLMs) are at the forefront ...
VentureBeat

This new framework lets LLM agents learn from experience, no fine-tuning required

A new learning paradigm developed by University College London (UCL) and Huawei Noah’s Ark Lab enables large language model (LLM) agents to dynamically adapt to their environment without fine-tuning ...
marktechpost

Memory-R1: How Reinforcement Learning Supercharges LLM Memory Agents

Large language models (LLMs) now stand at the center of countless AI breakthroughs—chatbots, coding assistants, question answering, creative writing, and much more. But despite their prowess, they ...
Semiconductor Engineering

LLM Inference: Core Bottlenecks Imposed By Memory, Compute Capacity, Synchronization ...

A new technical paper titled “Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need” was published by NVIDIA. “This paper presents a limit study of ...