Articles tagged "ローカルAI"

Gemma 4 hardware requirements at a glance: E2B/E4B need 5GB VRAM, 26B MoE 16GB, 31B Dense 24GB (Q4) or 62GB (FP16). Covers RTX 3060 to H100, Apple Silicon M1-M4, CPU-only operation, Mac/Windows/Linux setups, recommended GPUs, and budget tiers — current as of Q2 2026.

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.

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.

A comprehensive guide to building high-performance AI agents with Qwen3.5-9B using Mac mini M4 and OpenClaw. Covers hardware requirements, LINE/Slack/Discord integration, and performance benchmarks.

Comprehensive guide to Qwen3.5-9B: Ollama setup instructions, hybrid Gated DeltaNet + Sparse MoE architecture, 262K context window, GPQA 81.7 and IFBench 76.5 (beating GPT-5.2's 75.4), comparison with GPT-4o-mini and Claude Haiku, and practical business use cases. Runs on just 5GB RAM.

A complete step-by-step guide to installing Qwen3.5-9B locally on Mac, Windows, and Linux. Covers setup via Ollama, llama.cpp, and vLLM, along with quantization options (GGUF Q4/Q5/Q8), GPU acceleration (CUDA/Metal), Docker deployment, API server configuration, performance tuning, and troubleshooting. Practical instructions for developers and IT administrators looking to run SLMs on-premises.

A thorough TCO comparison between running Qwen3.5-9B as a local AI and using cloud APIs. Covers hardware costs, electricity, maintenance, break-even analysis, and ROI calculation frameworks for SMBs in Shinagawa, Minato, and Shibuya.

Gartner has named Domain-Specific Language Models a top strategic technology trend for 2026. Small Language Models (SLMs) are transforming AI adoption for SMBs with lower costs, higher accuracy for specific tasks, and zero data leakage risk. This guide covers benefits, leading models, practical use cases, and step-by-step adoption.