🧠 Running LLMs Locally with Ollama in 2025

Date Created: 2024-09-06
Updated: 2025-04-28
By: 16BitMiker
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In 2025, local large language model (LLM) development continues to surge, and tools like Ollama are leading the charge. Whether you’re an app developer, researcher, or hobbyist, Ollama makes it easier than ever to run powerful LLMs on your own machine—no cloud dependency required.

Let’s walk through what Ollama is, how it has evolved, and what you need to know to get up and running in 2025.

📦 What is Ollama?

Ollama is a command-line tool and runtime environment that simplifies running LLMs locally. It wraps models, configuration, and serving infrastructure into a self-contained system. Unlike traditional model deployment pipelines, Ollama provides a frictionless experience:

• 🧊 No fiddling with Python environments or dependency hell.

• 🔄 Models are packaged declaratively using a Modelfile.

• ✅ Fast setup with built-in serving and API support.

It’s especially popular among developers who want to prototype with LLMs quickly and securely—without sending data to third-party services.

🚀 What's New in 2025?

Ollama has seen rapid development since 2024. Notable updates in 2025 include:

• ✅ Support for quantized multi-modal models (text + vision).

• ✅ Integrated GPU/CPU auto-detection and fallback.

• ✅ Dockerless container-style isolation for models.

• ✅ Model streaming via Ollama Daemon using WebSocket and HTTP APIs.

• ✅ Full support for Llama 3, Gemma, and Codestral 2024 series.

• ✅ Easier sharing of fine-tuned models via ollama push to private or public registries.

These updates make Ollama not just a tool for running models, but a full ecosystem for experimenting and deploying AI locally.

📋 Installation Guide

🐧 Linux (Debian-based)

For most Debian-based systems (Ubuntu 20.04+), install with:

curl -fsSL https://ollama.ai/install.sh | sh

This script:

• Downloads the latest binary to /usr/local/bin/ollama

• Sets up the Ollama service as a systemd unit

• Verifies architecture compatibility (x86_64 or Apple Silicon)

🍎 macOS (Intel & M1/M2)

Use Homebrew for easier package management:

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brew install ollama

Or, download the .pkg installer directly from ollama.ai.

🖥️ Windows (Preview)

As of Q2 2025, Ollama for Windows is in public beta. It supports WSL2 and native execution via PowerShell:

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Invoke-WebRequest -Uri https://ollama.ai/windows-install.ps1 -UseBasicParsing | Invoke-Expression

Note: GPU acceleration on Windows requires NVIDIA RTX GPUs with CUDA 12.2+.

▶️ Running Your First Model

Once installed, you can immediately run a model:

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ollama run llama3

If the model isn’t downloaded yet, Ollama will fetch it automatically. You can also pull models explicitly:

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ollama pull gemma:2b-instruct

To list all installed models:

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ollama list

To remove a model:

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ollama rm llama3

📚 Modelfiles: What They Are & How to Use Them

A Modelfile is a declarative file similar to a Dockerfile. It describes:

• The base model

• System prompts

• Parameters like temperature, top_p, and max tokens

• Optional fine-tuned weights or LoRA adapters

Example:

​x
FROM llama3:8b-instruct

PARAMETER temperature=0.7
PARAMETER top_p=0.9

SYSTEM "You are a helpful assistant. Answer concisely."

Build the model package locally:

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ollama create my-custom-assistant -f Modelfile

Then run:

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ollama run my-custom-assistant

🔄 Shell Integration & Automation

Ollama works seamlessly with Unix pipelines and scripting:

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# Pipe input from a file
cat prompt.txt | ollama run mistral

# Save output to a file
ollama run llama3 > response.txt

# Automate with bash
for model in llama3 gemma mistral; do
    echo "Testing $model"
    echo "What's the capital of Canada?" | ollama run $model
done

You can also run Ollama remotely over SSH:

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ssh user@192.168.1.99 'ollama run llama3'

🧠 Model Landscape in 2025

✅ Supported Models

Ollama supports a growing number of open-access models:

• 🧩 LLaMA 3 (Meta): 8B, 70B, instruction-tuned and base

• 💡 Gemma (Google): 2B and 7B, highly efficient

• 🛠️ Codestral (Mistral): Code-focused model trained on 80+ languages

• 🌪️ Mistral & Mixtral: Open-weight models with strong performance

• 🧪 Phi-3 (Microsoft): Tiny transformer fine-tuned for reasoning tasks

• 🧬 TinyLlama & Orca-Mini: Optimized for edge devices and low RAM

Ollama will automatically quantize models to fit your hardware using GGUF formats.

🖥️ System Requirements

You can run smaller models CPU-only, but GPU acceleration is recommended for latency-sensitive tasks.

🔧 Troubleshooting Tips

• ❗ If Ollama fails to start, check the daemon logs:

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  journalctl -u ollama.service
  

• ❌ Out of memory errors? Use smaller quantized variants like llama3:8b-q4.

• 🕸️ Networking issues? Make sure port 11434 (default) is open for API access.

• 🧪 Test model inference locally:

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  curl http://localhost:11434/api/generate -d '{"model": "llama3", "prompt": "Hello!"}'
  

👥 Why Use Ollama?

• 🔐 Privacy: Keep your data local—no third-party data exposure.

• 🧰 Simplicity: No complex Python setups or external servers.

• ⚡ Speed: Low-latency responses, especially with GPU support.

• 🧪 Flexibility: Mix and match models, tune prompts, run batch jobs.

• 📡 Offline AI: Ideal for air-gapped systems or disconnected workflows.

✅ Summary

Ollama brings the power of LLMs to your local machine—with minimal setup, fast performance, and broad model support. Whether you're building a chatbot, automating documentation, or just exploring AI, Ollama lets you do it all without relying on external services.

With support for Llama 3, Gemma, Codestral, and more, 2025 is the perfect time to dive into local AI development.

📚 Read More

• 🔗 Ollama Official Docs

• 🔗 Meta’s LLaMA 3 Paper

• 🔗 Google Gemma Overview

• 🔗 Mistral.ai

• 🔗 GGUF Format Details

• 🔗 Awesome LLMs on GitHub

Happy hacking! 🧠💻