All the articles with the tag "llm".
Your app calls OpenAI, your side project calls Anthropic, your homelab whispers to Ollama — and your codebase looks like a crime scene. LiteLLM and vLLM are the dynamic duo that puts a single sane API in front of every model you'll ever run, local or cloud.
Stop paying per-token to ask questions about your own documents. This guide walks you through building a fully local RAG pipeline with Ollama and ChromaDB — from Docker Compose to Python code — so your AI can actually know things without hallucinating them.
LangChain does everything and LlamaIndex does one thing brilliantly. Here's how to pick the right RAG framework without regretting it at 2 AM.
Most people use OpenAI's embeddings because it's easy. But local embeddings exist. How to pick and when it actually matters.
Before you download a 70B model, calculate if it fits. The formulas, the gotchas, and a quick calculator you can actually use.
RAG is the default answer for giving LLMs access to documents. But chunking, embedding, and retrieval introduce failure modes that a virtual filesystem sidesteps entirely.
Google's Gemma 4 is the best open model they've shipped yet. Here's how to pull it, run it, and actually use it for real work with Ollama on your own hardware.
1-bit models store weights as -1, 0, or 1. That sounds insane until you see them run a 100B parameter model on a laptop CPU. Here's what's actually happening.
AMD finally has a fast, open source local LLM server that uses both GPU and NPU. If you've been jealous of Nvidia users, Lemonade is worth your time.
JSON mode forces models to output valid JSON. When it's a lifesaver vs. when it's overkill and makes the model worse.
Temperature and top_p control randomness in LLMs. No probability theory needed. Just practical intuition and how to tune them.
vLLM, llama.cpp, and Ollama all run local LLMs — compare throughput, memory use, GPU support, and which fits your hardware.
