Ollama vs LM Studio vs vLLM vs llama.cpp: Qual usar em 2026?
Four tools, four jobs. Ollama and LM Studio are experience layers, llama.cpp is the engine, and vLLM is a production server. Here’s exactly which one to pick — and when.
Modelos de Linguagem de Grande Porte
Deep dives into large language models, prompt engineering, and AI chatbots
Requisitos do sistema para o Ollama em 2026: Quanta RAM e VRAM você realmente precisa
The number-one reason a model fails to run isn’t a bug — it’s memory. Here’s exactly how much RAM and VRAM each Ollama model size needs, and a formula to know before you download.
Como instalar o Ollama em 2026: Mac, Windows e Linux (passo a passo)
Installing Ollama takes about two minutes on any OS. Here are the exact steps for Mac, Windows, and Linux, plus how to run your first model and fix the errors people actually hit.
Os melhores LLMs locais para executar no Ollama em 2026 (classificados por caso de uso)
Ollama can run 100+ models, but you only need a handful. Here are the best local LLMs in 2026 ranked by what you’re actually trying to do — and the VRAM each one needs.
O melhor LLM local para programação em 2026 (testado em tarefas reais)
A local coding model means your code never leaves your machine. Here are the best ones in 2026 — ranked by SWE-bench, hardware needs, and how they handle real refactors.
Modelos de Linguagem de Grande Porte de Código Aberto versus Código Fechado em 2026: Uma Comparação Completa
Should you build on an open model like Llama or a closed API like GPT? This guide compares open and closed LLMs on the things that actually decide the choice.
Alucinações em Modelos de Linguagem de Grande Porte em 2026: Por que ocorrem e como evitá-las
Why do AI models confidently make things up? This guide explains what causes LLM hallucinations, the different types, and the proven techniques to reduce them.
Engenharia de Prompt em 2026: 12 Técnicas que Realmente Funcionam
Prompt engineering isn’t magic words — it’s a set of repeatable techniques. Here are the 12 that genuinely improve AI output in 2026, with examples and when to use each.
RAG Explicado: Como a Geração Aumentada por Recuperação Funciona em 2026
RAG is the technique behind almost every AI system that answers questions from your own documents. This guide explains how retrieval-augmented generation works — clearly, and without the jargon.
Fine-tuning versus RAG em 2026: quando usar cada abordagem (e quando usar ambas)
Fine-tuning and RAG are the two ways to customize a language model — and they solve different problems. This guide gives you a clear framework for choosing the right one.
