This article discusses the challenges of productionizing Ollama to handle concurrent users, focusing on rate limits, cloud fallback, and cost guardrails. It offers solutions for issues like request queues, latency spikes, and lack of budget control when running local LLMs.
This hands-on guide details how to set up and run Google's Gemma 4, an open multimodal LLM, on your laptop using Ollama in about 5 minutes. It covers understanding the model's architecture, interacting via the terminal, sending images, and utilizing its "thinking mode".
This article delves into Ollama's configuration engine, explaining how to fine-tune local language model parameters.
This guide provides a step-by-step tutorial for beginner and intermediate developers on how to build an AI-powered Learning Management System from scratch. It leverages technologies like Ollama, FastAPI, and React.
This content details how to deploy a hybrid LLM API architecture, combining Claude with local models like Ollama for cost optimization. It outlines the setup to intelligently route calls based on real-time cost thresholds, significantly reducing inference spend while maintaining response quality.
This article describes building a voice-controlled AI agent locally, utilizing Whisper for speech-to-text and Ollama for understanding user intent and performing actions. The agent automates tasks like file creation, code generation, and summarization, all managed through a Streamlit user interface.
O texto introduz o VoxAgent, um agente de IA local e controlado por voz, projetado para ir além das demos tradicionais de IA ao executar ações seguras e visíveis em uma máquina real. Ele suporta entrada de microfone, processamento local de fala, entendimento de intenção e tarefas como criação de arquivos e geração de código.
An automation engineer shares their first AI testing project, building a FastAPI service with a local LLM (Ollama/llama3.2) and a pytest suite, prompted by a recruiter's job offer. The goal was to understand the nuances of AI/LLM testing compared to traditional UI/API testing, with the suite's initial success making the learning experience challenging.
Este conteúdo descreve como construir agentes de IA úteis em um backend Go usando LangChainGo para integrar ferramentas que buscam dados e acionam ações. Ele utiliza Ollama com o modelo qwen3.5:9b para execução local, incluindo instruções de configuração e uma introdução à definição de ferramentas no LangChainGo.
The author integrated a local evaluation loop using an Ollama model into their personal self-hosted AI assistant to score interactions based on accuracy, relevance, and appropriate confidence. After analyzing 800 interactions, they discovered that shorter, more direct answers consistently received higher scores.
This article details the best open-source models for the Hermes Agent, focusing on self-hosted setups to eliminate API costs. It highlights Llama 4 Maverick for overall quality, Qwen 3 8B for budget VPS, and Mistral Small as a lightweight option, all optimized for local function calling via Ollama.
This article provides a practical guide on building a local Retrieval Augmented Generation (RAG) system. It demonstrates how to integrate Ollama for large language models with ChromaDB as a vector database, aiming for a quick setup in just 30 minutes.
This article ranks Alibaba's Qwen models for use with the Hermes Agent, highlighting Qwen3 Max for cloud deployments due to its strong reasoning and tool-calling capabilities, and Qwen3 8B for local use via Ollama on 8GB RAM hardware. It emphasizes the flexible deployment options of the open-source Qwen models.
BizNode employs Ollama (Qwen3.5) to run locally on user hardware, ensuring that data never leaves the machine. This provides true AI privacy, keeping user information secure.
This article is a comprehensive guide on how to use Ollama to run large language models (LLMs) locally, for free and privately. It details the process of deploying powerful AI assistants on your personal computer, including hardware requirements and basic commands.
The author attempted to install OpenClaw on Windows with Ollama for a writing challenge, encountering a warning about native Windows support and an error related to a missing Node.js module. This led to skepticism and a frustrating initial experience.