LLM

Author(s): compensation Originally published on Towards AI. Large language models (LLMs) are powerful, but they require significant hardware resources to run locally. Many users rely on open-source models because of …

Image by author # Introduction Large language models got really fast when Grok introduced its own custom processing architecture called the Grok Language Processing Unit LPU. These chips were designed …

I’ve spent the last five years working in Boston’s tech scene, but my journey in AI and machine learning has taken me to Glasgow, Toronto, and roles at companies like …

Last updated on February 12, 2026 by Editorial Team Author(s): Nikhil Originally published on Towards AI. Super Bowl LX is the first time the foundation model and AI assistant were …

Serving large language models (LLMs) at scale is a major engineering challenge due to key-value (KV) cache management. As models grow in size and logic capacity, the KV cache footprint …

Author(s): neel shah Originally published on Towards AI. As an AI engineer, designing systems that interact with large language models (LLMs) like Google’s Gemini is a daily challenge. LLM API …

Last updated on February 3, 2026 by Editorial Team Author(s): Tanveer Mustafa Originally published on Towards AI. Building an LLM from Scratch: 7 Essential Types and a Complete Implementation Guide …

In this tutorial, we build a robust, multi-layered security filter designed to protect large language models from adaptive and interpretable attacks. We combine semantic similarity analysis, rule-based pattern detection, LLM-driven …

We begin this tutorial by configuring a high-performance evaluation environment, specifically focused on integrating depthval Framework for bringing unit-testing rigor to our LLM applications. Bridging the gap between raw recovery …

Estimate scaling has become one of the most effective methods to improve the quality and accuracy of answers in deployed LLMs. The idea is simple. If we are willing to …