Running Gemma 4 Locally with Ollama on Your PC

2026-04-08 · Source: Analytics Vidhya · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Google has released Gemma 4, an open-weight family of language models designed for local execution, offering enhanced reasoning, efficiency, and multimodal support for text and images, with some variants extending to audio and video. The models are suitable for privacy-sensitive and offline applications. Gemma 4 includes four variants: E2B (2.3B effective parameters), E4B (4.5B effective parameters), 26B A4B (3.8B active parameters, Mixture-of-Experts architecture), and 31B (30.7B active parameters, Dense Transformer architecture), each with specific hardware requirements ranging from 8GB RAM for edge devices to 24GB+ VRAM for the 31B variant. The article details setting up Gemma 4 locally using Ollama and demonstrates its application in building a "Second Brain" AI project with Claude Code CLI for document processing, embedding, RAG querying, and summarization.

Key takeaway

For AI Engineers and Machine Learning Engineers considering local LLM deployments, Gemma 4 offers a viable option for privacy-sensitive and offline applications. While local models like `gemma4:26b` can be resource-intensive and may struggle with complex code generation tasks, the `gemma4:31b-cloud` variant provides a more robust solution for intricate development workflows. You should evaluate your specific hardware and task complexity before committing to a purely local setup, as cloud-backed models may still be necessary for efficient, complex project completion.

Key insights

Gemma 4 offers open-weight, locally runnable LLMs with multimodal capabilities and diverse architectures for varied hardware.

Principles

• Local LLMs enhance privacy and reduce costs.
• Mixture-of-Experts (MoE) improves model manageability.
• Hardware scales with model size and complexity.

Method

Install Ollama, pull Gemma 4 variants, and use `ollama run` for local inference. Integrate with Claude Code CLI by launching it with `ollama launch claude --model gemma4:[variant]` for local AI-powered development.

In practice

• Use E2B/E4B for 8GB RAM laptops.
• Target 26B A4B for 16GB+ VRAM workstations.
• Utilize 31B on Apple Silicon Macs (24GB+ VRAM).

Topics

• Gemma 4
• Ollama
• Local LLMs
• Claude Code CLI
• Second Brain AI

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Analytics Vidhya.