AI

If you’re planning to run open-weight LLMs locally or in production, one of the first questions is: How much GPU VRAM do I actually need? The answer depends on three major components: Model weights KV cache (context memory) Runtime overhead Let’s break each one down clearly and practically. 1️⃣ Model Weights: The Base Memory Cost The largest fixed memory cost comes from the model weights. Simple Formula Weights (GB) ≈ Parameters (in billions) × (bits per weight / 8) ...

Frontier vision models have gotten really good at understanding images — but they’ve also had a consistent weakness: They still often treat an image like a single static glance. So if the answer depends on something tiny (a serial number, a distant street sign, a gauge reading, a small UI label), the model might miss it… and then it has to guess. Google’s new capability called Agentic Vision, launched with Gemini 3 Flash, is a major step toward fixing that. ...

Large language models (LLMs) like GPT-4, Llama, or Grok generate text by running inference — the phase where a trained model produces outputs from a given input prompt. While training is resource-intensive and done once, inference happens every time a user sends a query. Understanding the mechanics of inference is key to grasping why some models feel “fast” while others lag, and why certain optimizations matter. At a high level, modern LLM inference (for autoregressive transformer-based models) splits into two distinct phases: prefill and decode. These phases behave very differently in terms of computation and directly affect two critical user-facing metrics: Time to First Token (TTFT) and Inter-Token Latency (ITL). ...

Recently, someone shared a screenshot on x.com, how to download OpenAI Home Directories. I tried it, and it works. In this blog, we will now try to understand exactly what the contents of this home directory are. working with GPT-5.2 thinking with gpt 5.2, i got error zip file not found. https://t.co/c1zTfBlWb9 pic.twitter.com/85tEv28MuJ — Nitin Kalra (@nkalra0123) <a href="https://twitter.com/nkalra0123/status/1999771366397231386?ref_src=twsrc%5Etfw">December 13, 2025</a> Let’s analyse the contents Inside the open ai home directory oai/ Folder: Slides, Docs, PDFs, and Spreadsheets Tooling This folder is a small toolkit for working with common “office” artifacts – PowerPoint decks, DOCX files, PDFs, and spreadsheets. It combines a few Python utilities with a set of practical guides that describe the preferred tools and a quality-check workflow (render → visually inspect → iterate). ...

Hallucinations in RAG (Retrieval-Augmented Generation) chatbots can undermine user trust and lead to misinformation. In this comprehensive guide, we’ll explore proven strategies to minimize these AI-generated inaccuracies and build more reliable chatbot systems. If you’re building a RAG chatbot, you’ve likely encountered the frustrating problem of hallucinations—when your AI confidently provides incorrect or fabricated information. The good news? There are effective, battle-tested solutions to dramatically reduce these errors. Let’s dive into the multi-layered approach that actually works. ...

Introduction When training large language models (LLMs) the most important question is simple: how do we measure whether the model is doing well? For regression you use mean squared error, for classification you might use cross-entropy or hinge loss. But for LLMs — which predict sequences of discrete tokens — the right way to turn “this output feels wrong” into a number you can optimize is a specific kind of probability loss: categorical cross-entropy / negative log likelihood, and the closely related, more interpretable metric perplexity. ...

Introduction In the attention mechanism used by Large Language Models (LLMs) like transformers (e.g., GPT), the core idea is to allow the model to dynamically focus on relevant parts of the input sequence when generating or understanding text. This is achieved through a process called scaled dot-product attention, where input tokens (e.g., words or subwords) are transformed into three types of vectors: Q K V, Query (Q), Key (K), and Value (V). These are not arbitrary; they’re learned projections of the input embeddings via linear transformations matrices ...

Introduction Token embeddings (aka vector embeddings) turn tokens — words, subwords, or characters — into numeric vectors that encode meaning. They’re the essential bridge between raw text and a neural network. In this post, below we will run a small demos (Word2Vec-style analogies, similarity checks), and provide concrete PyTorch code that demonstrates how an embedding layer works, I also include a tiny toy training loop so you see embeddings updated by backprop. ...

Introduction In this blog post, we dive deep into tokenization, the very first step in preparing data for training large language models (LLMs). Tokenization is more than just splitting sentences into words—it’s about transforming raw text into a structured format that neural networks can process. We’ll build a tokenizer, encoder, and decoder from scratch in Python, and walk through handling unknown tokens and special context markers. By the end, you’ll not only understand how tokenization works but also have working Python code you can adapt for your own projects. ...

Ever wondered how advanced AI models can tackle truly complex problems with a depth of analysis that seems to mimic human thought? The secret lies in a groundbreaking capability known as “thinking.” This fascinating development is designed to unblock key bottlenecks on the path to greater intelligence in AI. Moving Beyond Fixed Compute Historically, powerful large language models (LLMs) were designed to respond immediately to requests. This meant they applied a constant amount of computing power at “test time”—the moment you ask a question or give a command—to generate a response. This fixed compute budget restricted how deeply the model could “think” about a problem, limiting its ability to handle extremely hard or challenging tasks. Imagine if your brain only spent a fixed millisecond on every problem, no matter its complexity! ...