Optimization
TikTok Researchers Introduce SWE-Perf: The First Benchmark for Repository-Level Code Performance Optimization
Introduction As large language models (LLMs) advance in software engineering tasks—ranging from code generation to bug fixing—performance optimization remains an elusive frontier, especially at the repository level. To bridge this gap, researchers from TikTok and collaborating institutions have introduced SWE-Perf—the first benchmark specifically designed to evaluate the ability of LLMs to optimize code performance in…
A new paradigm for AI: How ‘thinking as optimization’ leads to better general-purpose models
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 Researchers at the University of Illinois Urbana-Champaign and the University of Virginia have developed a new model architecture that could lead to more robust AI systems with more powerful…
Computer and network-attached storage: Capacity optimization and backup expansion
Motivations for bolstering backup Last March, I documented my travails striving to turn a 2018-era x86-based Apple Mac mini into a workable system in spite of its diminutive, non-upgradeable 128 GByte SSD’s internal capacity: Eight months later (last November), I discussed how, motivated by the lightning-induced last-summer demise of one of my network-attached storage (NAS)…
A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization
Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as 4-bit quantization and LoRA (Low-Rank Adaptation). In this tutorial, we walk through a practical implementation…
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…
10 Google Shopping Product Feed Optimization Tips & Tricks
Google Shopping isn’t just about bidding and budget management – it’s about feeding Google the best possible data. Unlike traditional search ads, where keywords dictate targeting, Shopping campaigns rely on your product feed. The quality, accuracy, and completeness of your product data determine how often and where your ads appear. A well-optimized feed improves impressions,…
Fine-Tuning an Open-Source LLM with Axolotl Using Direct Preference Optimization (DPO) — SitePoint
LLMs have unlocked countless new opportunities for AI applications. If you’ve ever wanted to fine-tune your own model, this guide will show you how to do it easily and without writing any code. Using tools like Axolotl and DPO, we’ll walk through the process step by step. What Is an LLM? A Large Language Model…
Voice Search Optimization At Scale: A Guide For Enterprise Marketers
Smartphones put the world at our fingertips. People have questions that need answering, as well as the services or products they need. All of these things are just a search away, and now, we’ve seen a cosmic shift from traditional search to voice search and voice assistants. Statistically, voice search and assistants are not something…