Llm

DeepSeek recently open-sourced DeepSpec, a codebase for training and evaluating speculative decoding draft models, along with the DSpark paper and checkpoints. I wanted to understand what is actually new here, because speculative decoding itself is not new. The older idea is already powerful: use a small draft model to propose tokens, then let the large target model verify them. I explained that foundation in Speculative Decoding Explained. DSpark is interesting because it attacks two practical problems that show up when speculative decoding moves from a paper idea into production serving: ...

Speculative decoding is one of the most important tricks behind fast LLM serving. The basic idea is simple: Use a small fast model to guess several future tokens, then use the original large model to verify those guesses in one pass. If the guesses are right, the large model moves forward by multiple tokens instead of one. If a guess is wrong, the large model corrects it and the system continues. ...

The KV cache is one of those LLM inference terms that sounds simple until someone asks you to explain it from first principles. At a high level, the idea is: During generation, a transformer stores the previously computed key and value vectors, so it does not recompute them for the whole sequence every time it predicts the next token. That is the short answer. But the short answer hides the important part: why keys and values are reusable, why queries are not cached in the same way, and why this matters so much for latency. ...

When people talk about transformers, they usually focus on attention, scale, or training data. But one smaller design choice has an outsized effect on model quality: How does the model know where each token appears in the sequence? That question matters because transformers do not understand order by default. Without positional information, a sequence starts to look more like an unordered set of tokens than a structured sentence, paragraph, or program. ...

Large Language Models (LLMs) such as GPT-2, GPT-3, LLaMA, and BERT are built on top of the Transformer architecture. That architecture changed natural language processing by replacing recurrence with attention, which lets models process sequences more efficiently and capture long-range relationships more directly. If you are trying to understand what terms like layer, transformer block, and attention head actually mean, the easiest way is to follow the path a sentence takes through a GPT-style model. ...

If you are building a RAG system, internal knowledge assistant, or document search chatbot, one question matters more than almost anything else: When the answer is supposed to come from the provided documents, how often does the model still make things up? That is exactly what the March 9, 2026 paper “How Much Do LLMs Hallucinate in Document Q&A Scenarios? A 172-Billion-Token Study Across Temperatures, Context Lengths, and Hardware Platforms” tries to measure. ...

Attention is the idea that made modern transformers practical and powerful. Instead of compressing an entire input into one fixed vector, a model can decide, token by token, which earlier pieces of information matter most right now. That sounds simple, but there are many different kinds of attention mechanisms, and they exist because models face different constraints: some need strong alignment between an encoder and a decoder some need to generate text one token at a time without looking ahead some need to handle very long documents some need to reduce GPU memory traffic at inference time This article walks through the main families of attention, shows where they fit, and explains why newer variants such as DeepSeek’s multi-head latent attention (MLA) matter. ...

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. ...

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). ...