LLMs
Swapping LLMs isn’t plug-and-play: Inside the hidden cost of model migration
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Swapping large language models (LLMs) is supposed to be easy, isn’t it? After all, if they all speak “natural language,” switching from GPT-4o to Claude or Gemini should be as simple as changing an API key……
A Coding Implementation on Introduction to Weight Quantization: Key Aspect in Enhancing Efficiency in Deep Learning and LLMs
In today’s deep learning landscape, optimizing models for deployment in resource-constrained environments is more important than ever. Weight quantization addresses this need by reducing the precision of model parameters, typically from 32-bit floating point values to lower bit-width representations, thus yielding smaller models that can run faster on hardware with limited resources. This tutorial introduces…
Bigger isn’t always better: Examining the business case for multi-million token LLMs
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The race to expand large language models (LLMs) beyond the million-token threshold has ignited a fierce debate in the AI community. Models like MiniMax-Text-01 boast 4-million-token capacity, and Gemini 1.5 Pro can process up to 2…
Could LLMs help design our next medicines and materials?
The process of discovering molecules that have the properties needed to create new medicines and materials is cumbersome and expensive, consuming vast computational resources and months of human labor to narrow down the enormous space of potential candidates. Large language models (LLMs) like ChatGPT could streamline this process, but enabling an LLM to understand and…
Researchers teach LLMs to solve complex planning challenges
Imagine a coffee company trying to optimize its supply chain. The company sources beans from three suppliers, roasts them at two facilities into either dark or light coffee, and then ships the roasted coffee to three retail locations. The suppliers have different fixed capacity, and roasting costs and shipping costs vary from place to place….
NVIDIA AI Researchers Introduce FFN Fusion: A Novel Optimization Technique that Demonstrates How Sequential Computation in Large Language Models LLMs can be Effectively Parallelized
Large language models (LLMs) have become vital across domains, enabling high-performance applications such as natural language generation, scientific research, and conversational agents. Underneath these advancements lies the transformer architecture, where alternating layers of attention mechanisms and feed-forward networks (FFNs) sequentially process tokenized input. However, with an increase in size and complexity, the computational burden required…
How Well Can LLMs Actually Reason Through Messy Problems?
The introduction and evolution of generative AI have been so sudden and intense that it’s actually quite difficult to fully appreciate just how much this technology has changed our lives. Zoom out to just three years ago. Yes, AI was becoming more pervasive, at least in theory. More people knew some of the things it…
How Yelp reviewed competing LLMs for correctness, relevance and tone to develop its user-friendly AI assistant
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The review app Yelp has provided helpful information to diners and other consumers for decades. It had experimented with machine learning since its early years. During the recent explosion in AI technology, it was still encountering…
Reinforcement Learning Meets Chain-of-Thought: Transforming LLMs into Autonomous Reasoning Agents
Large Language Models (LLMs) have significantly advanced natural language processing (NLP), excelling at text generation, translation, and summarization tasks. However, their ability to engage in logical reasoning remains a challenge. Traditional LLMs, designed to predict the next word, rely on statistical pattern recognition rather than structured reasoning. This limits their ability to solve complex problems…
A closer look at LLM’s hyper growth and AI parameter explosion
The rapid evolution of artificial intelligence (AI) has been marked by the rise of large language models (LLMs) with ever-growing numbers of parameters. From early iterations with millions of parameters to today’s tech giants boasting hundreds of billions or even trillions, the sheer scale of these models is staggering. Table 1 outlines the number of…
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