Ollama Setup Made Easy for Local Coding in 4 Steps

Alexandra

Employee Experience (EX) Manager, WPRiders

Published: July 29, 2025

TL;DR

This guide shows developers how to set up a local AI coding assistant using Ollama and NeuronAI—a PHP framework—on their machine to get fast, private, offline AI help without any subscriptions.

• Install Ollama to run language models locally (no cloud dependency).
• Pick the right AI model (e.g., CodeLlama for PHP or Phi-2 for low RAM).
• Use NeuronAI to build a customizable PHP agent that interacts with your chosen model.
• Integrate with VS Code using the Continue.dev extension for seamless AI-powered autocomplete, inline edits, and chat—all without sending code to external servers.
• Benefit: full privacy, zero latency, and no monthly fees, while enhancing your daily development workflow.

You know that moment when you’re deep in a coding session, about to ask your AI assistant for help, and suddenly your internet connection decides to take a break? Your productivity grinds to a halt while you wait for the connection to return—or worse, you’re stuck troubleshooting without any AI assistance at all.

This frustration is exactly why local AI solutions like Ollama have become such a game-changer for developers who want reliable, always-available coding help. Instead of depending on cloud services that require constant connectivity, you can run powerful language models directly on your machine. No subscriptions. No internet requirements. No sending your proprietary code to third-party servers.

Think about it—wouldn’t it be nice to have an AI coding assistant that works whether you’re on a plane, in a coffee shop with spotty WiFi, or just prefer keeping your code completely private?

Ollama makes this possible by running lightweight AI models locally on Mac, Windows, and Linux. Pair it with NeuronAI, a PHP framework built specifically for creating AI agents, and you get something pretty special: complete control over your coding assistant without monthly fees or privacy concerns.

The setup we’re about to build doesn’t just give you offline access to AI help. It changes how you work with code entirely. You’ll have models that understand PHP, Python, C++, Java, TypeScript, C#, and Bash—all running privately on your hardware.

For PHP developers, especially, this approach solves a real problem. You get sophisticated AI assistance without signing up for yet another monthly service. Your code stays on your machine, your workflow stays uninterrupted, and your wallet stays happier.

Ready to build your own local coding assistant? Let’s walk through exactly how to set this up.

Getting Your Local AI Environment Ready

Before we dive into building your coding assistant, let’s talk about why this setup makes so much sense—and what you’ll need to make it work smoothly.

Why Run AI Models on Your Own Machine?

Here’s the thing about local language models: they solve problems you might not even realize you have with cloud-based AI services.

Your code stays completely private. No sending proprietary algorithms to external servers. No wondering if your client’s sensitive business logic is being stored somewhere in the cloud. When you’re working on mission-critical projects, this privacy isn’t just nice to have—it’s essential.

Speed becomes a non-issue too. Cloud-based AI tools often have that frustrating delay while your request travels across the internet, gets processed, and comes back. Local models respond instantly because everything happens on your machine.

But let’s be honest about the trade-offs. Running these models locally means your hardware does the heavy lifting. You’ll need at least 8GB of RAM for smaller 7B parameter models, 16GB for 13B models, and 32GB or more for the larger 33B models. It’s like having a powerful workshop in your garage instead of renting tools from someone else—more initial investment, but complete control over your workspace.

The setup and maintenance require some technical know-how, too. This isn’t a “set it and forget it” solution, but the payoff in privacy and reliability makes it worthwhile for serious PHP development work.

Installing Ollama: The Simple Approach

Ollama keeps installation straightforward across all major platforms. Here’s how to get it running:

For macOS:

brew install ollama

For Linux:

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

For Windows: Download the installer directly from the official Ollama website.

Once installed, verify everything works by running:

ollama --version
ollama list

These commands confirm Ollama is properly installed and show any models you’ve already downloaded. The system creates an isolated environment that won’t interfere with your existing software, running quietly in the background until you need it.

Picking the Right Model for PHP Work

Model selection can make or break your local AI experience. Choose too large, and your system crawls. Choose too small, and you’ll get disappointing results.

For PHP-specific tasks, CodeLlama hits the sweet spot. It’s designed specifically for code generation and understands PHP syntax patterns better than general-purpose models. Download it with:

ollama pull codellama

Working with limited hardware? Phi-2 offers surprising capability in a compact package. It handles small to medium PHP tasks without overwhelming your system resources.

For comprehensive PHP development, consider these options based on what your hardware can handle:

• Llama 3.3: Meta’s latest offering with variants from lightweight 8B to powerful 70B parameters
• DeepSeek-R1: Excels at reasoning through complex coding problems, available from 1.5B to 70B parameters
• Qwen2.5: Strong multilingual support with excellent mathematical reasoning

The rule of thumb? Match your ambitions to your hardware. An 8GB machine running a 33B model will frustrate you with slow responses. Better to use a smaller, responsive model that keeps your development flow smooth.

Your local environment is now ready to become something much more powerful than just another development tool—it’s about to become your personal coding partner.

Building Your PHP AI Agent

Now comes the fun part—creating a PHP AI agent that actually talks to your local Ollama models. This is where NeuronAI shines. It’s an open-source PHP package that handles all the complex AI interactions so you don’t have to reinvent the wheel.

Getting NeuronAI Installed

NeuronAI works with whatever PHP setup you’re already using. Laravel, Symfony, plain PHP—doesn’t matter. The framework is framework-agnostic, which means you can drop it into existing projects without breaking anything.

Installation is as simple as any other Composer package:

composer require inspector-apm/neuron-ai

That’s it. You now have access to everything you need to build AI-powered applications. What makes NeuronAI particularly nice is its minimal external dependencies—no massive dependency trees that conflict with your existing code. Whether you’re working on a small side project or a large enterprise application, the same patterns work everywhere.

Creating Your First Agent

Think of an AI agent like a specialized assistant that knows exactly what its job is. With NeuronAI, you create agents by extending the base Agent class:

<?php
namespace App;

use NeuronAI\Agent;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Ollama\Ollama;

class MyAgent extends Agent 
{
    public function provider(): AIProviderInterface 
    {
        return new Ollama(
            url: 'http://localhost:11434/api',
            model: 'llama3.2:latest',
        );
    }
}

This code tells your agent where to find the AI model and which one to use. Notice how we’re pointing to localhost:11434—that’s your local Ollama server. You could swap this for OpenAI or Anthropic later if needed, but right now we’re keeping everything local.

Here’s something important to understand: AI agents work differently from regular functions. Traditional code gives you the same output for the same input every time. AI agents work with probabilities, so you might get slightly different (but equally valid) responses. NeuronAI handles this complexity behind the scenes with error handling and retry logic.

Teaching Your Agent What to Do

Your agent needs clear instructions about its role and behavior. This is where the SystemPrompt class becomes incredibly useful:

public function instructions(): string 
{
    return new SystemPrompt(
        background: [
            "You are a PHP code assistant specialized in generating clean, modern code."
        ],
        steps: [
            "Understand the user's coding request thoroughly.",
            "Generate PHP code that follows PSR standards.",
            "Explain how the code works when appropriate."
        ],
        output: [
            "Provide code with proper indentation and comments.",
            "Include explanations for complex logic or design decisions."
        ]
    );
}

The SystemPrompt class organizes your instructions into three logical parts:

• Background: What is this agent’s job?
• Steps: How should it approach tasks?
• Output: What should the response look like?

These instructions get sent to the model with every request, ensuring your agent behaves consistently. No complex prompt engineering required—just clear, structured guidance.

Testing Your Agent

Once everything’s set up, using your agent is straightforward:

$response = MyAgent::make()->chat(new UserMessage("Write a function to validate email addresses"));
echo $response->getContent();

Your local AI agent will analyze the request, generate PHP code that follows your instructions, and return a helpful response. All of this happens on your machine, with your data staying completely private.

You’ve just built a custom coding assistant that works offline, respects your privacy, and costs nothing to run beyond your electricity bill.

Getting Your Local AI Agent Running

Your agent is built, Ollama is installed, and now comes the exciting part—actually firing up your local AI assistant. This is where all the setup work pays off.

Starting Your Ollama Server

First things first: Ollama needs to be running before your PHP agent can do anything useful. The good news? Starting it is straightforward.

# Launch Ollama as a background service
ollama start

# Or run in the foreground for debugging
ollama serve

Once it’s running, open http://localhost:11434 in your browser. You should see a simple confirmation page—nothing fancy, but it means your server is alive and ready to handle requests from your PHP agent.

Your First AI Conversation

Now for the moment of truth. Create a test file to see if everything actually works:

<?php
try {
    $agent = MyAgent::make();
    $response = $agent->chat(new UserMessage('Write a PHP function to validate email addresses'));
    echo $response->getContent();
} catch (Exception $e) {
    echo 'Error: Unable to communicate with the AI service. Please ensure Ollama is running.';
}

Run it from your terminal:

php my-agent.php

If everything clicks into place, you’ll see a nicely formatted PHP function appear in your terminal. The first run might feel a bit slow—that’s just the model loading into memory. After that, responses come much faster.

When Things Go Wrong (And They Sometimes Do)

Let’s be honest: even with careful setup, things can get wonky. Here are the most common hiccups and how to fix them.

Connection refused errors happen when your script can’t reach Ollama. Double-check that the server is actually running and accessible at the expected URL.

Memory issues are more frustrating. If your system starts crawling or responses take forever, you’re probably running a model that’s too big for your hardware. 7B parameter models: 8GB RAM minimum, 13B models need 16GB, and anything larger requires 32GB or more.

When debugging gets tough, check the logs:

• Mac: cat ~/.ollama/logs/server.log
• Linux: journalctl -u ollama --no-pager
• Windows: Look in %LOCALAPPDATA%\Ollama

The reality is that local AI depends entirely on your hardware. Choose a model that fits your system, and you’ll get stable, responsive AI help. Push beyond your limits, and you’ll spend more time troubleshooting than coding.

But when it works? It’s pretty amazing to have your own AI assistant running privately on your machine.

Bringing AI Into Your Daily Workflow

Your local PHP AI agent works great on its own, but the real productivity boost comes when you connect it directly to your code editor. This turns occasional AI help into a constant coding companion that’s always within reach.

Getting Continue.dev Running in VS Code

Continue.dev bridges the gap between your local Ollama setup and your development environment. It’s an open-source extension that turns your editor into an AI-powered workspace without sending any data to external servers.

The extension gives you three main ways to interact with your AI:

• Tab autocomplete that suggests entire functions or code blocks as you type
• Inline editing, where you highlight code and ask for specific changes
• Chat interface for broader questions about your codebase or architecture decisions

Installation is straightforward. Open VS Code, press Ctrl+P (Windows/Linux) or Cmd+P (macOS), type ext install continue.continue, and hit Install. You’ll see the Continue icon appear in your sidebar once it’s ready.

Connecting Continue to Your Local Ollama

Here’s where your local setup really shines. Instead of configuring API keys for cloud services, you just point Continue to your local Ollama instance.

Click the gear icon in the Continue sidebar to access settings, then add this configuration:

{
  "models": [
    {
      "title": "CodeLlama",
      "provider": "ollama",
      "model": "codellama",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "apiBase": "http://localhost:11434/",
    "title": "Starcoder2 3b",
    "provider": "ollama",
    "model": "starcoder2:3b"
  }
}

This setup uses CodeLlama for general coding help and the lighter Starcoder2 model for quick tab completions. The dual-model approach keeps your editor responsive while giving you powerful AI assistance when you need it.

Making AI Part of Your Coding Flow

Once configured, you have multiple ways to get AI help without breaking your concentration:

For quick edits: Highlight any code and press Ctrl+I (Windows/Linux) or Cmd+I (macOS). Ask for improvements, explanations, or modifications in plain English.

For bigger questions: Use the Continue sidebar to chat about your code. Type @codebase to give the AI context about your entire project when asking architectural questions.

For ongoing help: Just keep coding. Continue will suggest completions as you type, which you can accept with Tab or ignore if they don’t fit.

The beauty of this setup is how natural it feels. You’re not switching between applications or breaking your flow—the AI becomes part of your editor, responding instantly because everything runs locally.

You can even create custom commands for repetitive tasks like generating unit tests or refactoring functions. These shortcuts turn common development patterns into single keystrokes.

With Continue.dev handling the editor integration, your local Ollama setup becomes a true coding partner that works exactly how you want it to.

Getting More from Your Local AI Setup

Your basic setup works great, but there’s always room to squeeze more performance and functionality from your local coding assistant. These tweaks can make the difference between a decent tool and something that genuinely changes how you write code.

Finding the Right Model for Your Hardware

Not all models perform the same way on your machine. DeepSeek-R1 excels at complex reasoning tasks, but those capabilities come with resource demands that scale from 1.5B to 70B parameters. Llama 3.3 offers more balanced performance across different sizes, while CodeLlama stays particularly sharp for PHP and general coding work.

Here’s the reality: hardware matters more than you might think. You’ll need at least 8GB RAM for 7B parameter models, 16GB for 13B models, and 32GB for larger 33B versions. When you’re choosing between more GPU cores versus memory, remember that cores speed up evaluation while higher memory lets you load bigger models.

The sweet spot? Match your model size to your actual coding needs. A smaller, faster model that responds instantly often beats a larger, slower one that makes you wait.

Running Ollama on Remote Hardware

Sometimes your laptop just isn’t powerful enough. That’s where remote Ollama instances become handy. You can run Ollama on a cloud GPU server while keeping the interface local on your development machine.

Set up your remote instance to bind to localhost:11434 on your cloud server. Then establish secure tunneling from your development machine to that endpoint. This approach gives you access to more powerful hardware while maintaining most privacy benefits—your code stays in your controlled environment.

Remote instances also work well for team collaboration. Everyone can use the same model versions and configurations without individual hardware limitations getting in the way.

Privacy and Security Reality Check

Local AI keeps your code private—that’s the whole point. But even local setups need some security thinking. For high-security projects, consider network isolation. Choose models with clear data policies when possible.

Here’s something worth knowing: studies show 83% of firms already use AI for code generation. Security teams are paying attention to AI-generated code quality. The smart approach? Always review what your AI suggests. Implement proper code review processes to catch potential vulnerabilities before they make it into production.

Your local setup gives you control, but it doesn’t eliminate the need for good development practices.

Making Your Agent Do More

The real power comes from customization. NeuronAI lets you extend your agents with specialized tools for generating unit tests, refactoring code, or analyzing performance patterns.

Build in proper logging and error handling to monitor how your agent performs over time. This data helps you improve accuracy and catch issues early. Also consider input validation—you want to prevent unintended interactions or potential security problems in your agent setup.

The goal isn’t just to have a working AI assistant. It’s to have one that gets better at helping you with your specific coding patterns and requirements.

Your Local AI Coding Setup is Ready

You’ve just built something pretty remarkable. What started as frustration with unreliable internet connections and monthly subscription fees has become a completely private, always-available coding assistant that runs entirely on your hardware.

The setup you’ve created with Ollama and NeuronAI isn’t just another development tool—it’s a shift in how you approach coding problems. Your code never leaves your machine. Your AI assistant works whether you’re online or offline. And you’re not paying monthly fees for the privilege.

But here’s what’s really changed: you now have complete control over your AI assistance. Want to fine-tune responses for your specific coding style? You can do that. Need to ensure your proprietary code stays completely private? Already handled. Working on a project that requires absolute data security? You’ve got it covered.

The Continue.dev integration we walked through transforms this from a nice-to-have into something that feels natural—like having a knowledgeable colleague who never gets tired of answering questions and never judges your debugging approach at 2 AM.

Sure, running AI locally means being mindful of your hardware limitations. A 7B model on 8GB of RAM won’t match the raw power of cloud-based services with unlimited resources. But for most PHP development work, these local models are more than capable—and they come with benefits that cloud services simply can’t offer.

The real power emerges as you customize your setup. Your agent can learn your coding patterns, understand your project structure, and provide increasingly relevant suggestions. It’s like training a junior developer who happens to have instant access to vast programming knowledge.

Most PHP developers I know spend at least $20-30 monthly on various AI coding tools. You’ve just eliminated that ongoing cost while gaining better privacy and offline capability. Not a bad trade-off.

Your local PHP AI agent is ready to use. Fire it up, start coding, and see how it feels to have AI assistance that truly works on your terms.

Key Takeaways

Setting up local AI coding with Ollama and NeuronAI transforms PHP development by providing privacy-focused, offline-capable AI assistance that runs entirely on your machine.

• Install Ollama locally to run AI models privately without cloud dependencies, requiring 8GB RAM for 7B models and 16GB for larger ones.

• Use NeuronAI framework to create PHP AI agents with a simple Composer installation and extend the base Agent class for custom functionality.

• Integrate Continue.dev with VS Code for seamless AI assistance, including tab completion, inline editing, and chat functionality directly in your editor.

• Choose appropriate models like CodeLlama for PHP tasks or Phi-2 for resource-constrained systems to match your hardware capabilities.

• Maintain complete data privacy as your code never leaves your device, eliminating security concerns while reducing subscription costs.

This local setup provides immediate AI responses, works offline, and offers unlimited usage without monthly fees, making it an ideal solution for developers prioritizing privacy and cost-effectiveness in their coding workflow.

Glossary

FAQs

Q1. How can I set up a local AI coding assistant using Ollama? 

To set up a local AI coding assistant with Ollama, first install Ollama on your machine. Then, choose an appropriate model like CodeLlama for PHP tasks. Install the NeuronAI framework using Composer, create a PHP AI agent by extending the base Agent class, and configure it to use Ollama as the provider. Finally, start the Ollama server and run your PHP script to interact with the local AI assistant.

Q2. What are the hardware requirements for running Ollama models locally? 

The hardware requirements depend on the size of the model you choose. Generally, you need at least 8GB of RAM for 7B parameter models, 16GB for 13B models, and 32GB or more for larger 33B+ models. For optimal performance, match your model choice with your available hardware resources.

Q3. Can I integrate Ollama with my code editor for a seamless workflow? 

Yes, you can integrate Ollama with your code editor using tools like Continue.dev for VS Code. This integration provides features such as tab autocomplete, inline editing, and a chat interface for asking questions about your code. Configure Continue.dev to use your local Ollama instance for a privacy-focused, offline-capable AI coding assistant.

Q4. What are the advantages of using a local AI coding assistant over cloud-based alternatives? 

Local AI coding assistants offer several advantages, including enhanced privacy as your code never leaves your device, reduced latency for faster responses, the ability to work offline, and no subscription costs. You also have complete control over the models and can customize them to your specific needs.

Q5. Which Ollama model is best for PHP coding tasks? 

For PHP coding tasks, the CodeLlama model is particularly effective as it specializes in code generation and completion across multiple programming languages, including PHP. If you’re working with limited hardware resources, Phi-2 offers a compact yet powerful alternative for small to medium-sized programming tasks.

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