VentureBeat

Nvidia’s new technique cuts LLM reasoning costs by 8x without losing accuracy

Researchers at Nvidia have developed a technique that can reduce the memory costs of large language model reasoning by up to eight times. Their technique, called dynamic memory sparsification (DMS), ...
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 ...
VentureBeat

DeepSeek’s conditional memory fixes silent LLM waste: GPU cycles lost to static lookups

When an enterprise LLM retrieves a product name, technical specification, or standard contract clause, it's using expensive GPU computation designed for complex reasoning — just to access static ...
blockchain

List of AI News about LLM memory

According to Stanford AI Lab (@StanfordAILab), the newly released TTT-E2E framework enables large language models (LLMs) to continue training during deployment 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

LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation

We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories into reusable memory ...
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 ...
marktechpost

Building a Context-Folding LLM Agent for Long-Horizon Reasoning with Memory Compression and ...

In this tutorial, we explore how to build a Context-Folding LLM Agent that efficiently solves long, complex tasks by intelligently managing limited context. We design the agent to break down a large ...
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 ...
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 ...