---
title: "Google Gemma 2 · Ollama Blog"
url: https://daily.dev/posts/google-gemma-2-ollama-blog-vjp1ryzyz
source_url: https://ollama.com/blog/gemma2
type: article
source: "Ollama"
published: 2026-08-23T12:23:55.841Z
updated: 2026-08-23T12:55:43.797Z
tags: ["llm", "langchain", "ollama"]
reading_time: 1
upvotes: 0
comments: 0
language: en
---

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# Google Gemma 2 · Ollama Blog

**[Ollama](https://daily.dev/sources/ollama)** · 1 min read · 0 upvotes · 0 comments

## Summary

Google's Gemma 2 model family is now available to run locally via Ollama in three sizes: 2B, 9B, and 27B parameters. Google claims the 27B model outperforms models more than twice its size thanks to a new architecture. The post shows how to pull and run each size with a single command and how to integrate the model with LangChain and LlamaIndex.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://ollama.com/blog/gemma2>

## Questions this post answers

### How do I run Google Gemma 2 locally with Ollama?

Use the command 'ollama run gemma2' to run the default 9B parameter version. For the smaller 2B model, run 'ollama run gemma2:2b', and for the larger 27B model, run 'ollama run gemma2:27b'. All three sizes are available through Ollama's model library.

_daily.dev surfaces practical guides for developers experimenting with local LLM setups like this one._

### What sizes does Google Gemma 2 come in?

Gemma 2 is released in three parameter sizes: 2B, 9B, and 27B. The 27B version features a new architecture and reportedly delivers performance surpassing models more than twice its size in benchmarks, positioning it as a leading open model at that scale.

_Comparing open model sizes and benchmarks is easier when developers track releases like Gemma 2 on daily.dev._

### How do I use Gemma 2 with LangChain or LlamaIndex?

In LangChain, import Ollama from langchain_community.llms, instantiate it with model="gemma2", then call llm.invoke() with a prompt. In LlamaIndex, import Ollama from llama_index.llms.ollama, instantiate the same way, and call llm.complete() with a prompt. Both integrations route requests through the locally running Ollama model.

_daily.dev helps developers stay current on integrating new open models into their LLM tooling stack._

---

Tags: [#llm](https://daily.dev/tags/llm), [#langchain](https://daily.dev/tags/langchain), [#ollama](https://daily.dev/tags/ollama)

[View this post on daily.dev](https://daily.dev/posts/google-gemma-2-ollama-blog-vjp1ryzyz)