Replace GitHub Copilot with Local Llama 3 on MacBook: Setup Guide

What You'll Learn

• How to verify macOS version, chip architecture, and storage before installing Ollama.
• How to pull a Llama 3 model and confirm it responds on the local Ollama API.
• How to configure the Continue extension in Visual Studio Code with the Ollama provider.
• Which trade-offs remain around performance, context length, privacy boundaries, and maintenance.

Teams evaluating whether to replace github copilot with local llama 3 macbook setups usually want three things at once. They want less reliance on a hosted inference service. They want editor integration that feels close to a familiar assistant. And they want a configuration they can actually maintain. This article separates the moving parts, calls out what the official documentation supports, and flags claims that the sources do not prove.

Three components need to be understood separately. Ollama is a local model runner for macOS that manages downloads and exposes a local API. Llama 3 is a family of open weight models from Meta that Ollama can run when the model is available in the Ollama library. Continue is a Visual Studio Code and JetBrains extension that connects to model providers, including Ollama, using a configuration file. Mixing these names up leads to setup errors later.

1. Confirm macOS version and Mac architecture

The Ollama macOS documentation lists macOS Sonoma 14 or newer as the supported baseline. It states that Apple M series machines use CPU and GPU acceleration, while x86 Intel Macs are supported in a CPU only mode. Before doing anything else, open the Apple menu, choose About This Mac, and record the macOS version and the chip. If the Mac is older than Sonoma 14, upgrade macOS first or accept that the tooling is outside the documented support window.

The architecture matters because CPU only inference on Intel Macs will be materially slower than Apple Silicon inference, and the official documentation does not promise that any specific model size will feel interactive on either configuration. Treat performance as something you will measure on your own hardware rather than something the documentation guarantees. If you are also comparing hosted alternatives, our developer tested review of Cursor vs GitHub Copilot vs Windsurf outlines what a cloud assistant currently offers.

2. Plan storage before pulling any model

The Ollama macOS documentation explicitly warns that model files can require tens to hundreds of gigabytes of storage. That range depends on which models and which quantizations you pull, not on marketing claims. A small coding model is a very different footprint from a large general model, and pulling several models compounds quickly. Decide up front whether you will keep one model or several.

Plan for three storage buckets. First, the Ollama application itself and its runtime files. Second, each pulled model, which lives in Ollama storage until you remove it. Third, any embedding models you add for editor features. If disk pressure becomes a problem later, you can remove models with the Ollama command line rather than reinstalling. For a broader view of efficient inference stacks, our summary of what is new in vLLM shows how server side runtimes evolve alongside local ones.

3. Install Ollama the documented way

The official Ollama macOS documentation describes mounting the ollama.dmg file and moving the app into the Applications folder. Follow that procedure from the official site at docs.ollama.com/macos rather than a third party mirror. Once the app is in Applications, launch it once so that the background service is registered on the system. Ollama then exposes a local HTTP API that other tools, including Continue, can call.

Package manager installs may work for some users, but the documented path is the disk image. If you plan to support this setup across several machines, standardize on the documented path so that troubleshooting stays consistent. Our earlier walkthrough of a related local workflow, how to run Muse Glimmer locally, shows the same principle of preferring vendor documented steps.

4. Pull a Llama 3 model and verify it responds

Once Ollama is installed, use the command line to pull a Llama 3 model that is available in the Ollama library. Choose a variant that fits your storage plan and that you are willing to test on your Mac. After the pull completes, run the model interactively in the terminal and issue a short prompt. If it returns a response, the local runtime is working. If it fails, the error will usually point at storage, permissions, or an unsupported macOS version.

This step is worth doing before any editor integration. Editor problems and runtime problems have very different fixes, and mixing them together during a first setup makes troubleshooting harder. Keep the terminal test as your baseline for future debugging.

5. Understand the local Ollama API surface

Ollama exposes a local HTTP endpoint that other applications call over the loopback interface. This is what makes editor integration possible without a hosted service. It is also what allows a second machine on your network to act as a shared model host, since Continue documents an optional apiBase setting for a remote Ollama instance in its provider documentation.

Two decisions follow from this design. First, decide whether the model will run on the same Mac as your editor or on a different machine. Second, decide whether you want that endpoint to remain strictly local. Do not assume that binding beyond localhost is safe by default. If you decide to share an Ollama server across a team, treat it like any other internal service and apply appropriate network controls. Modular server patterns are discussed in our Nutanix MCP server guide.

6. Install and configure the Continue extension

Install the Continue extension from the Visual Studio Code marketplace. Open its configuration and add a model entry that uses the Ollama provider. The Continue documentation for the Ollama provider, available at docs.continue.dev, documents the provider name, the model identifier, and the optional apiBase field. Use the model identifier exactly as it appears when you pulled the model in Ollama.

Save the configuration and open the Continue side panel. Send a small prompt to confirm that Continue reaches Ollama and that a response streams back. If nothing happens, the failure is almost always a mismatched model name, a missing pull, or an Ollama service that has not been launched. The official Meta Llama repository at github.com/meta-llama/llama3 is a useful reference for model naming conventions.

7. Separate autocomplete from chat

Chat and inline autocomplete are different workloads. Chat models are optimized to hold a conversation. Autocomplete typically benefits from a fill in the middle capable base model that predicts tokens in context rather than answering a question. Continue supports configuring a separate model for tab autocomplete, and you should treat that entry as an independent choice.

Do not assume that any Llama 3 variant will behave identically to a hosted assistant for either task. The Continue and Ollama documentation do not promise feature parity with a commercial product. Test each capability, then decide whether to keep both features enabled, only chat, or only autocomplete. For a related discussion of specialized model choices, see our overview of NVIDIA Nemotron 3.5 Lightning.

8. Tune context length to fit your Mac

The Continue provider documentation notes that you can reduce context length when system memory is insufficient. This matters because context length has a direct effect on memory pressure. If your Mac begins to swap, if responses stall, or if the runtime is killed, shortening context is one of the first knobs to try. Increase it again only if measurements show headroom.

You may also need to set capabilities such as tool use or image input explicitly when Continue cannot infer them from the model identifier. The documentation calls this out. Treat capability flags as configuration you own, not as automatic behavior. Our note on Maple Preview and DeepGrove ternary models gives further examples of how model design shapes practical configuration.

9. Troubleshoot without changing everything at once

When something breaks, change one variable at a time. First check that Ollama itself still answers in the terminal. If it does not, the runtime is the problem. If it does, restart Visual Studio Code and confirm that Continue is loaded. Then check the model identifier in the Continue configuration against the identifier that Ollama shows. Most failures resolve at one of these three checkpoints.

Keep a short log of the exact commands you used to install, pull, and configure. That log is more valuable than any blog post when you rebuild the environment on a new machine. If you want a broader troubleshooting mindset for local AI stacks, review our Muse Glimmer local run guide.

10. Be careful with privacy language

Running a model on your own Mac reduces reliance on a hosted inference service. It does not, by itself, prove that the setup is fully offline, fully private, or fully compliant with a specific regulation. The Ollama and Continue documentation describe how the components work. They do not certify a privacy posture for your organization. That posture depends on how you configure the network, how you handle logs, and what other extensions you have installed in Visual Studio Code.

If privacy is the driving reason for the migration, write down the specific claims you need to satisfy and then verify each one against configuration and network behavior on your own machine. Treat vendor documentation as a starting point rather than a certification.

11. Plan for maintenance and updates

Local model runners and editor extensions release updates frequently. Set a simple maintenance rhythm. Update Ollama on a predictable schedule, note the version, and re run your terminal smoke test before opening the editor. Update the Continue extension separately and re run a small chat and autocomplete test. If both still work, you are done. If either breaks, you can identify which component regressed because you did not change them together.

Model choices also drift. New model releases may be added to the Ollama library, and older ones may fall out of favor. Do not chase every release. Pin the models your team relies on, evaluate replacements on a schedule, and only swap when a new model clearly improves your day to day work.

12. Conclusion and honest expectations

A local setup based on Ollama, a Llama 3 model, and the Continue extension can be a workable alternative to a hosted coding assistant for some workflows. The official documentation supports the core steps of installing Ollama on macOS Sonoma 14 or newer, pulling a model, and configuring Continue with the Ollama provider. What the documentation does not support are blanket claims of zero cost, identical Copilot behavior, guaranteed privacy, or uniform performance across every Mac. Approach the migration as an experiment you measure on your own hardware, keep the components clearly separated in your mind, and update the setup on a schedule you can maintain.