Articles tagged "Ollama"

Start with Q4_K_M; step up to Q5_K_M or Q6_K if you have VRAM headroom. This guide explains GGUF naming, the quality/speed/VRAM tradeoffs per level, IQ (imatrix) quants, and how to choose by task. Updated July 2026.

A 2026 comparison of Ollama and LM Studio, the two leading local LLM runners: CLI vs GUI, install and usage, API, OS support, and model formats, with guidance on which to pick.

**Open GENAI** (the OSS release of Japan's government generative-AI platform **源内 / GENAI**) was published by Japan's Digital Agency on **April 24, 2026 on GitHub under MIT license** ([official release](https://www.digital.go.jp/en/news/907c8e5d-2f4f-4bd7-9400-37c9f4221d7d) / [GENAI Web repo](https://github.com/digital-go-jp/genai-web) / [GENAI AI apps repo](https://github.com/digital-go-jp/genai-ai-api)). **What's released**: - **GENAI Web**: an AI interface built on TypeScript / React 19 / Zustand 5 / React Router 7 / AWS CDK / Tailwind CSS, with the **Digital Agency design system** - **GENAI AI Apps**: three development templates — **AWS for administrative RAG / Azure for self-hosted LLM / Google Cloud for a legal-system AI** referencing current statutes **License**: **MIT + CC BY 4.0** (commercial use, modification, and redistribution allowed) **Scale**: the foundation of a 2026-fiscal-year **pilot covering ~180,000 government employees across all ministries**, with planned expansion to local governments and private-sector adopters. **Design thesis**: **REST API-first + an ExApp (external-app integration) microservices model**, unifying AWS / Azure / Google Cloud behind a single interface. The goal is to **structurally eliminate vendor lock-in and duplicate development across agencies**. **vs GenU (AWS Generative AI Use Cases)**: where GenU leans into AWS managed services (Bedrock Agents / Knowledge Base / MCP), GENAI is **REST-API + ExApp-extensible**, adding **enterprise-grade governance** — team management with RBAC (System Admin / Team Admin / User), SAML multi-IdP, KMS CMEK, TTL data-retention policies, multi-layer WAF, Bedrock Inference Profiles. The trade-off: features like video generation, web extraction, and prompt optimization are **not built in** but delegated to external ExApps. **Validated models**: Claude Sonnet 4.6, Amazon Nova Lite. **[PLaMo 3.0 Prime](../columns/plamo-3-0-prime-pfn-japanese-llm-2026-06)** has also been selected as a trial model. **Caveat**: the Digital Agency explicitly states that **"permanent maintenance is not guaranteed and the OSS publication may be terminated in the future."** Long-term operations are the adopting organization's responsibility. **Fully-local community fork**: **[hirokawaguchi/open-genai](https://github.com/hirokawaguchi/open-genai)** (unofficial, experimental, MIT) swaps Cognito → **Keycloak (SAML)**, Bedrock → **OpenAI-compatible APIs (Ollama / vLLM / LM Studio)**, OpenSearch → **Qdrant**, DynamoDB → **SQLite**, Transcribe → **faster-whisper**, Bedrock image → **Stable Diffusion** — running the full GENAI stack on **a single Docker Compose command** with zero cloud dependency. Recommended Japanese model: **Qwen2.5**. Supports macOS Apple Silicon (Metal), Linux + NVIDIA (CUDA), and CPU-only. **Oflight's view**: combined with the trends covered in our [Local LLM June 2026 Update](../columns/local-llm-landscape-2026-june-update), Open GENAI is **the frontrunner generative-AI platform for Japanese municipalities and public-sector adopters** — now via two distinct paths (the official cloud-deployed release plus the hirokawaguchi/open-genai local-deployment fork). The column closes with three direct inquiry funnels for Open GENAI evaluation, custom implementation, and ongoing maintenance.

Obsidian is a local-first knowledge management tool developed by Canada-based Dynalist Inc. Since commercial use became fully free in February 2025, it has become the top PKM choice for Japanese business professionals — IT consultants, engineers, researchers, and legal practitioners alike. This guide covers everything from basic Vault setup and the new 2026 Bases feature to local LLM integration via Ollama, Zettelkasten and PARA workflows, and Japanese-language caveats, so you can start today.

A practical migration guide to building a Claude replacement using Qwen 3.5-9B (refreshed Q2 2026). Apache 2.0 license, 262K context, runs on 16GB RAM. Complete coverage from Ollama setup to API migration, prompt conversion, cost comparison, and current limitations as of April 2026.

Comprehensive comparison of the top 10 local LLMs as of April 2026. Covers SWE-bench scores, Japanese language performance, VRAM requirements, Ollama commands, and licensing for Gemma 4, Llama 4, Qwen 3.5, GLM-5.1, Kimi K2.5, MiniMax M2.5, and more.

Complete guide to Google Gemma 4 (released April 2, 2026): 4 model variants (E2B/E4B/26B MoE/31B Dense), Apache 2.0 license, system requirements, multimodal capabilities, AIME 89% benchmark, 140+ languages, and step-by-step Ollama installation and setup instructions.

A practical implementation guide for SMBs to leverage Gemma 4 locally and eliminate cloud API costs entirely. Learn how to achieve cost savings with just 25-day ROI period, plus 5 concrete use cases including customer support, document generation, and development assistance.

By combining Ollama and OpenClaw, you can build AI agents on Mac mini that dynamically switch between multiple LLMs. This article provides detailed practical steps from Ollama installation to model management, OpenClaw integration configuration, and performance comparison. We introduce how to build a local AI infrastructure that can be adopted by SMBs and startups, especially in Shinagawa, Minato, Shibuya, Setagaya, Meguro, and Ota wards.

This article explains how to build an internal AI chatbot with zero API costs using Ollama and OpenClaw. We introduce implementation methods for cost reduction crucial to SMBs, integration with existing Slack and LINE, conversation memory, and FAQ automation. Centered in Shinagawa, Minato, Ota, and Meguro wards, we propose a zero-cost AI strategy that can start with existing Mac hardware.

This article explains how to build a RAG (Retrieval-Augmented Generation) customer support system by combining Ollama's embedding models with OpenClaw agents. Through vector database integration, you can generate accurate answers from FAQ documents and deploy AI support across multiple channels like LINE and Slack.

This article provides a detailed explanation of how to create business-specific custom AI models using Ollama's Modelfile feature and deploy them with OpenClaw. We cover practical techniques including prompt engineering, parameter tuning, importing GGUF format models, and A/B testing through multi-agent routing.

Build your own AI agent easily with Mac mini M4, Ollama, and OpenClaw. This beginner-friendly guide walks you through everything from Homebrew installation to model downloads and LINE/Slack integration, step by step. Supporting Tokyo-area businesses in Shinagawa, Minato, Shibuya, and beyond with AI agent implementation.