How to Use Gemma 4 in VS Code: Setup, Extensions, and Coding Workflows

Every developer I talk to has the same question: can I get a proper AI coding assistant without paying $19 a month and without sending my code to someone else's servers? The answer, as of mid-2026, is yes — and Gemma 4 running locally through Ollama inside VS Code is the best way to do it. Not a toy. Not a compromise you have to apologise for. A genuinely useful coding companion that lives entirely on your machine.

I have been running this setup full-time for over a month now, across Flutter, Python, and TypeScript projects. This guide is the result of that daily use — not a quick test, but a real workflow built on top of Gemma 4. I will walk you through four different ways to wire it into VS Code — including wiring Gemma 4 directly into GitHub Copilot Chat via its new local-model support — plus a fifth method that skips VS Code entirely and lets you use Gemma 4 from the terminal or PowerShell to analyse your entire project folder. I will show you which method I actually use every day, and give you the specific settings that make the experience feel fast rather than frustrating. If you have not installed Gemma 4 yet, start with my beginner's guide to running Gemma 4 locally and come back here once Ollama is running.

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Before You Start: Does Your Machine Have Enough Power?

Gemma 4 runs entirely on your hardware — there is no cloud fallback. If your machine cannot handle it, you will get painfully slow completions that break your flow instead of helping it. Here is what you actually need:

• Windows or Linux with an NVIDIA GPU: 8GB of VRAM is the sweet spot for the 12B model — that means cards like the RTX 3060 (12GB), RTX 4060 Ti (16GB), or RTX 4070 (12GB). If your GPU only has 4–6GB of VRAM, the smaller 4B model still works but you will notice the difference in code quality.
• Mac with Apple Silicon: Any M1, M2, M3, or M4 chip with 16GB of unified memory handles the 12B model comfortably. With 24GB you can run the 27B, and 32GB+ opens the door to the full 31B model.
• No dedicated GPU at all: The 4B model still runs on CPU, but expect 2–5 seconds per completion instead of under a second. You need 8GB of RAM minimum, 16GB preferred. It is usable for chat workflows but frustrating for inline autocomplete.

Quick hardware check: On Windows, press Ctrl+Shift+Esc to open Task Manager, go to "Performance" then "GPU" — look for "Dedicated GPU memory". On Mac, click the Apple menu and check "About This Mac" for your chip and memory. On Linux, run nvidia-smi in a terminal.

Install VS Code (Skip If You Already Have It)

If VS Code is already on your machine, jump straight to the Ollama section below.

1. Head to code.visualstudio.com and grab the installer for your OS.
   • Windows: Run the .exe. During installation, tick "Add to PATH" and "Register Code as an editor for supported file types" — both save you headaches later.
   • Mac: Unzip the download, drag VS Code into Applications. On first launch, right-click the icon and select "Open" to get past the macOS Gatekeeper warning.
   • Linux: Download the .deb package and run sudo dpkg -i code_*.deb, or install via snap with sudo snap install code --classic.
2. Open VS Code and press Ctrl+` (the backtick key above Tab) to open the integrated terminal. You will need this for the next steps.

Install Ollama — The Engine Behind Everything

Ollama is the piece that actually downloads and runs Gemma 4 on your machine. Think of it as a local server that sits quietly in the background, waiting for VS Code extensions to send it prompts. Every method in this guide relies on it.

1. Go to ollama.com and download the installer.
   • Windows: Run the .exe. After installation, Ollama starts automatically and shows up as an icon in your system tray (bottom-right corner, near the clock).
   • Mac: Open the .dmg, drag Ollama into Applications, and launch it. You will see its icon appear in the menu bar.
   • Linux: Run curl -fsSL https://ollama.com/install.sh | sh in your terminal. It installs and starts as a background service automatically.
2. Verify the installation: Open a terminal and run:

   ollama --version

   If you see a version number, you are good. If you get "command not found", restart your terminal or reboot your machine.
3. Confirm the server is live: Visit http://localhost:11434 in your browser. You should see the text "Ollama is running". If not, relaunch the Ollama app from your Start menu or Applications folder.

Download Gemma 4 — One Command, One-Time Download

This step downloads the model weights to your local disk. It only happens once — after that, the model loads from storage in seconds every time you start coding.

1. Open a terminal (or use the VS Code integrated terminal with Ctrl+`).
2. Pull the 12B model, which is the best balance of speed and quality for most developers (~7–8GB download):

   ollama pull gemma4

3. Limited VRAM or CPU only? Pull the lighter 4B model (~2–3GB): ollama pull gemma4:4b
4. Got 16GB VRAM? Pull the 27B for noticeably smarter completions (~16GB): ollama pull gemma4:27b
5. Got 24GB+ VRAM? Pull the flagship 31B — the strongest reasoning of any Gemma 4 variant (~19GB): ollama pull gemma4:31b
6. Verify the download: Run ollama list — your model should appear with its size.
7. Quick sanity check: Run ollama run gemma4 to open a chat. Ask it something simple like "Write a hello world in Python". If you get working code back, everything is set up correctly. Type /bye to exit.

Test Gemma 4 in the Ollama Desktop App (No VS Code Needed)

Recent Ollama builds ship with a built-in desktop chat window — it is the fastest way to confirm your install is working before you wire anything into VS Code. If the desktop app talks to Gemma 4 cleanly, every method below will work too, because all of them connect to the same local Ollama server at localhost:11434.

1. Open the Ollama app from your Start menu (Windows), Applications folder (Mac), or the system tray icon.
2. You will see a minimal chat interface with a model picker in the bottom-right. Click it and pick gemma4 — or whichever variant you pulled, such as gemma4:4b, gemma4:27b, or gemma4:31b.
3. Type a quick prompt like "Write a Python function that reverses a string" and press Enter. Gemma 4 should start streaming a response within a second or two.

Don't see a chat window? You are on an older Ollama build. Update to the latest version from ollama.com — the desktop chat UI is bundled with every fresh install. The CLI ollama run gemma4 command (above) still works on every version if you prefer staying in the terminal.

Method 1: Continue Extension — The Full Copilot Replacement (Recommended)

This is the method I use every day and the one I recommend to most developers. Continue gives you chat, inline code edits, and tab-autocomplete — essentially everything GitHub Copilot does, but pointed at your local Gemma 4 model. If you use Android Studio for Flutter work, the same Continue + Ollama setup works there too.

Setup

1. In VS Code, press Ctrl+Shift+X (Cmd+Shift+X on Mac) and search for Continue. Install the one published by Continue.dev.
2. Click the Continue icon in the left sidebar. The onboarding wizard launches and detects Ollama automatically — it lists every model you have pulled. Select Ollama as your provider.
3. If it prompts you to sign in, click Skip or Use local models. You do not need an account for local usage.
4. Pick Gemma 4 from the model dropdown at the top of the chat panel. Chat and inline edit work immediately after this step.

Enable tab autocomplete (important — it is off by default)

Continue's chat and inline edit features work out of the box, but tab autocomplete is not enabled by default. You need to configure it separately:

1. Open Continue's config file. Press Ctrl+Shift+P (Cmd+Shift+P on Mac), type Continue: Open Config, and select it. This opens either config.json or config.yaml depending on your version (the file lives in ~/.continue/ on Mac/Linux or C:\Users\YourName\.continue\ on Windows).
2. Add a tabAutocompleteModel entry. If your config is JSON:

   {
     "tabAutocompleteModel": {
       "title": "Gemma 4 Autocomplete",
       "provider": "ollama",
       "model": "gemma4"
     }
   }

   If your config is YAML:

   tabAutocompleteModel:
     title: Gemma 4 Autocomplete
     provider: ollama
     model: gemma4

3. Save the file. Continue reloads the config automatically — no VS Code restart needed.

Tip: For faster autocomplete, you can set a smaller model specifically for tab completions (like gemma4:4b) while keeping the larger 12B or 27B model for chat. Speed matters more than quality for inline suggestions.

Three shortcuts you will use constantly

• Chat about selected code: Highlight any block of code and press Ctrl+L (Cmd+L on Mac). Ask things like "explain this", "find bugs", or "what happens if the input is null?". You can also type @file or @codebase in the chat to reference other files without manually pasting them.
• Edit code inline: Highlight code, press Ctrl+I (Cmd+I on Mac), and type an instruction — "add error handling", "convert to async/await", "add TypeScript types". You get a diff to review before accepting.
• Tab autocomplete: Just start typing. Grey ghost text appears after a brief pause — press Tab to accept the suggestion, or keep typing to ignore it. Press Esc to dismiss.

Troubleshooting

• No suggestions or chat responses: Open http://localhost:11434 in your browser. If it does not say "Ollama is running", relaunch Ollama from your Start menu or Applications folder.
• Tab autocomplete not appearing: Make sure you have added the tabAutocompleteModel config as described above. Without it, only chat and inline edit will work.
• Suggestions are painfully slow: Run ollama ps in a terminal. If the processor column says cpu instead of gpu, switch to a smaller model like gemma4:4b or update your GPU drivers.

Method 2: CodeGPT Extension — Best for Chat-Heavy Workflows

If you spend more time asking questions about code than you do writing it — debugging, explaining legacy code, brainstorming architecture — CodeGPT is worth considering. It focuses heavily on the chat experience and has a cleaner conversation interface than Continue, though its inline completions lag behind.

Setup

1. Press Ctrl+Shift+X (Cmd+Shift+X on Mac), search for CodeGPT, and install it.
2. Click the CodeGPT icon in the sidebar and select "Ollama" as your AI provider.
3. CodeGPT scans for locally available models automatically. Pick Gemma 4 from the dropdown. If it does not show up, confirm Ollama is running with ollama list and click the refresh button.
4. Optional but recommended: Set a system prompt in CodeGPT's settings to steer the output quality:

   You are an expert software developer. Write clean, well-structured code. When explaining, break it down step by step.

5. Test it: Ask "Write a Python function that checks if a number is prime". If you get working code, the setup is complete.

How to use it

Highlight code in your editor, right-click, and you will see CodeGPT context menu options — "Explain this code", "Find bugs", "Refactor", "Generate tests". CodeGPT also preserves your conversation history across VS Code sessions, which is useful when you are working through a multi-step debugging problem over several hours.

Heads up: CodeGPT's tab-autocomplete with local models is noticeably less reliable than Continue's. If real-time inline suggestions matter to you, stick with Continue (Method 1) and use CodeGPT purely for chat.

Method 3: Ollama Extension — Minimal and Lightweight

If you just want a simple chat window to ask Gemma 4 questions without any extra features, the standalone Ollama extension is the fastest path. No accounts, no config files, no learning curve.

Setup

1. Press Ctrl+Shift+X, search for Ollama, and install the extension with the highest download count.
2. Press Ctrl+Shift+P (Cmd+Shift+P on Mac), type Ollama, and select "Ollama: Chat".
3. Pick Gemma 4 from the model list. If the list is empty, Ollama is not running — restart it.
4. Test: Ask "What does the map function do in JavaScript?" — if you get a coherent response, you are done.

This extension provides nothing beyond a chat panel — no inline autocomplete, no inline editing, no workspace indexing. That is the trade-off for its simplicity. It has almost zero impact on VS Code performance, which makes it a good choice for older machines. For the full experience, go with Continue (Method 1).

Method 4: Use Gemma 4 Locally Through GitHub Copilot (BYOK / Ollama Model)

If you already have a GitHub Copilot subscription but want specific prompts — sensitive code, client work, offline sessions — to run locally on Gemma 4, you do not need a second chat extension. VS Code's Copilot Chat now supports adding locally hosted Ollama models directly into the model picker, so you can flip between cloud Copilot and local Gemma 4 in the same chat panel without switching UIs. This is the setup I reach for when I want Copilot's editor integration but Gemma 4's privacy guarantees.

Setup

1. Make sure GitHub Copilot and GitHub Copilot Chat are installed and signed in. Press Ctrl+Shift+X (Cmd+Shift+X on Mac), search for GitHub Copilot Chat, and update to the latest version — local model support requires a recent build.
2. Confirm Ollama is running in the background. Open http://localhost:11434 in your browser and check that you see "Ollama is running". If not, relaunch the Ollama app.
3. Open Copilot Chat from the sidebar (or press Ctrl+Alt+I / Cmd+Ctrl+I). At the bottom of the chat panel, click the model picker dropdown (it shows the current model, e.g. "GPT-4o" or "Claude").
4. Click "Manage Models…" at the bottom of that dropdown. In the provider list that appears, select Ollama.
5. VS Code scans your local Ollama instance and lists every model you have pulled. Tick gemma4 (and any other variants you want available, such as gemma4:27b). Click OK.
6. Go back to the model picker at the bottom of the chat. Gemma 4 now appears in the list alongside the cloud Copilot models. Select it.

How to use it

Once selected, every Copilot Chat feature routes through your local Gemma 4 instead of GitHub's servers:

• Chat panel: Ask questions exactly as you would with cloud Copilot. @workspace, #file, and slash commands like /explain, /fix, /tests all work — they just hit your local model.
• Inline chat: Highlight code, press Ctrl+I (Cmd+I on Mac), and type an instruction. The diff preview and accept/reject flow are identical to the cloud experience.
• Edits across files: Use Copilot's Edits view (the pencil icon in the chat panel) to make multi-file changes. Gemma 4 12B handles small edits well; bump up to 27B if you want sharper results on multi-file refactors.

Important caveat about inline autocomplete: the model picker controls Copilot Chat, not the grey-ghost-text tab completions you see while typing. Those are still handled by GitHub's cloud Copilot model — you cannot currently swap them out for Gemma 4. If you want local tab autocomplete, stick with Continue (Method 1). This method is best thought of as "local chat + edits inside Copilot's UI".

Why I use this alongside Method 1

My daily setup is Continue for tab autocomplete (fast, local, always on) plus Copilot Chat with the Gemma 4 model selected for longer-form questions. Two reasons: first, Copilot's @workspace indexer is genuinely good at pulling in relevant files from across the repo, and it now does that with a local model answering the prompt. Second, switching between Gemma 4 and cloud Copilot is a one-click model-picker change — I keep the cloud model for quick generic questions and flip to Gemma 4 the moment I am working on anything I would not want leaving my machine.

Troubleshooting

• "Manage Models" option is missing: Update GitHub Copilot Chat. The BYOK/Ollama integration requires a recent release — older versions only show the cloud model list.
• Ollama not detected: Run ollama list in a terminal to confirm your models are visible. If the command works but VS Code sees nothing, restart VS Code entirely (close all windows) so it re-scans localhost:11434.
• Responses are much slower than cloud Copilot: That is expected — you are running a 12B+ parameter model on your GPU. Use ollama ps to confirm GPU offloading, and drop to gemma4:4b in the model picker if you need faster chat turnaround.
• Tab autocomplete did not change: That is correct behaviour. Only chat and inline-edit requests route to Gemma 4; ghost-text completions stay on cloud Copilot.

Method 5: Skip VS Code Entirely — Use Gemma 4 from the Terminal

This is the method most guides never mention, and it is one of the most practical. You do not need any VS Code extension at all. If Ollama is running, you can use Gemma 4 directly from PowerShell, Command Prompt, or any terminal to read your project files and get AI-powered code analysis, explanations, and suggestions.

Ask Gemma 4 about a single file

The simplest workflow: pipe a file's contents directly into Ollama with a prompt. Open a terminal in your project folder and run:

PowerShell:

Get-Content .\public\index.html -Raw | ollama run gemma4 "Explain what this code does and suggest improvements"

Bash / Git Bash / macOS / Linux:

cat public/index.html | ollama run gemma4 "Explain what this code does and suggest improvements"

The model sees your full prompt followed by the file contents. You can ask anything — "find bugs", "add accessibility attributes", "convert this to React", "explain the CSS layout" — and Gemma 4 responds in the terminal with its analysis.

Analyse multiple files at once

To feed several files in a single prompt, concatenate them with file markers so Gemma 4 knows which code belongs to which file:

PowerShell:

$allCode = Get-ChildItem -Path .\scripts -Recurse -Include *.js,*.py | ForEach-Object {
    "--- $($_.Name) ---`n$(Get-Content $_.FullName -Raw)"
}
$allCode -join "`n" | ollama run gemma4 "Review this code. Describe the architecture and flag any issues."

Bash:

find ./scripts -type f \( -name "*.js" -o -name "*.py" \) \
  -exec echo "--- {} ---" \; -exec cat {} \; \
  | ollama run gemma4 "Review this code. Describe the architecture and flag any issues."

Set a system prompt for better results

You can tell Gemma 4 to behave like a specific kind of reviewer by adding a --system flag:

Get-Content .\public\blog\gemma-4-vscode.html -Raw | ollama run gemma4 --system "You are a senior web developer and SEO expert. Be concise and specific." "Audit this HTML for SEO issues, accessibility problems, and performance improvements"

Build a reusable PowerShell function

If you find yourself doing this regularly, add a function to your PowerShell profile ($PROFILE) so you can ask Gemma 4 about any file with a single command:

function Ask-Code {
    param(
        [string]$Prompt,
        [string[]]$Files,
        [string]$Model = "gemma4"
    )
    $context = $Files | ForEach-Object {
        "--- $_ ---`n$(Get-Content $_ -Raw)"
    }
    ($context -join "`n") | ollama run $Model $Prompt
}

# Usage examples:
Ask-Code -Prompt "Find bugs in this code" -Files .\scripts\build_blog_cluster.py
Ask-Code -Prompt "Explain the relationship between these files" -Files .\public\index.html, .\public\animations.js
Ask-Code -Prompt "Suggest performance improvements" -Files .\public\blog\*.html -Model "gemma4:27b"

For serious codebase work: use Aider

If you want a full terminal-based AI pair programmer — one that understands your entire repo, tracks file context, and can make edits directly — Aider is the best option. It works with Ollama out of the box:

1. Install it: pip install aider-chat
2. Navigate to your project folder in the terminal.
3. Start a session:

   aider --model ollama_chat/gemma4

Aider automatically maps your entire repository, lets you add specific files to the conversation with /add filename, and applies code changes directly to your files with git-tracked diffs. It is the closest thing to having a full AI pair programmer in your terminal — no VS Code required. For batch automation that goes beyond interactive sessions — running the same prompt across hundreds of files, translating, summarising, or scripted refactors — see my four batch pipeline patterns for local AI workflows.

Context window limits to keep in mind

When piping files into Ollama, you are limited by Gemma 4's context window. A rough rule: 1 token is approximately 4 characters, so 128K tokens gives you roughly 500KB of code in a single prompt. For a typical web project, that covers dozens of files comfortably. For larger codebases, be selective about which files you include, or use Aider which handles intelligent file selection automatically.

Getting the Most Out of Inline Code Completions

Tab-autocomplete is where a local AI assistant either feels magical or feels like a drag on your workflow. The difference comes down to configuration. Here is what I have learned from a month of daily use:

• Speed beats quality for autocomplete. A 200ms suggestion that is 80% right is far more useful than a 3-second suggestion that is 95% right. You lose your train of thought waiting for the perfect completion. Start with the 12B model and drop to 4B if completions take longer than a second.
• Verify GPU offloading. This is the single most impactful performance check. Run ollama ps in a terminal — the output shows which processor is handling inference. If it says cpu instead of gpu, your completions will be 5–10x slower than they should be. On NVIDIA systems, make sure your drivers are current and set the OLLAMA_GPU_LAYERS environment variable if Ollama is not loading the full model onto the GPU.
• Shrink the context window. If completions feel sluggish even on GPU, reduce the context window in your extension settings. A window of 2048 tokens is more than enough for inline completions and significantly reduces memory pressure and latency.
• Enable fill-in-the-middle (FIM). Gemma 4 supports FIM natively, meaning it reads code both before and after your cursor to generate contextually aware suggestions. Continue has this on by default. If you are using another extension, check its settings — turning FIM on makes a noticeable difference in suggestion relevance.

Real Coding Workflows I Use Every Day

Autocomplete gets the headlines, but the chat interface is where Gemma 4 delivers the most practical value. These are the four workflows I reach for constantly:

Understanding inherited or unfamiliar code

Select a block of code — a function you did not write, a library method with a confusing signature, a regex that looks like hieroglyphics — and send it to the chat with Ctrl+L. Ask "Explain what this does step by step and flag anything that looks fragile." The 27B model handles this remarkably well across Python, JavaScript, TypeScript, Dart, Go, and Java. It identifies design patterns, traces control flow correctly, and catches things like missing null checks or silent exception swallowing.

Refactoring with guardrails

Highlight a function, press Ctrl+I, and type something like "Refactor this for readability. Extract magic numbers into named constants. Add types." Gemma 4 produces clean diffs that you review before accepting. One thing to watch for: it tends to over-decompose code into too many tiny functions. If that happens, a follow-up prompt like "simplify — keep it under three functions" pulls it back to something sensible.

Generating test scaffolds

Paste a function into the chat and ask: "Write unit tests for this using Jest. Cover the happy path, edge cases like empty input and null values, and error conditions." Gemma 4 produces usable test scaffolds that cover the main scenarios. It is not as thorough as Claude or GPT-4o at finding obscure edge cases — but it saves you the tedium of writing boilerplate assertions. Treat the output as a starting point, then add the edge cases you know matter for your specific use case.

Diagnosing errors from stack traces

Copy an error message and the surrounding code, paste both into the chat, and ask "What is causing this and how do I fix it?" This is where Gemma 4 consistently impresses me. Common runtime errors, dependency conflicts, type mismatches, off-by-one bugs — it nails the diagnosis on the first try more often than not. Stack traces and standard error patterns are well-represented in its training data, and the local model has the full context of your pasted code without any truncation.

Choosing the Right Model Size for Your Hardware

Getting this wrong is the number one reason developers try Gemma 4, have a bad experience, and give up. The right model depends on what you are using it for and what your machine can handle:

Model	Min VRAM	Autocomplete Speed	Code Quality	Best For
Gemma 4 4B	4GB	Very fast (100–300ms)	Basic — simple completions	Low-end GPUs, CPU-only machines, quick inline suggestions
Gemma 4 12B	8GB	Fast (300–800ms)	Good — handles most tasks well	Most developers — the best all-round choice
Gemma 4 27B	16GB	Moderate (800ms–2s)	Very good — complex logic and reasoning	Chat workflows, architecture questions, code reviews
Gemma 4 31B	24GB	Slower (1.5–3s)	Best — strongest reasoning ability	Difficult refactors, detailed explanations, less common languages

Concrete examples: an NVIDIA RTX 3060 (12GB VRAM) runs the 12B model with room to spare. The RTX 4070 (12GB) handles it even faster thanks to the newer Ada Lovelace architecture. Apple Silicon Macs with M1 Pro/Max and 16GB unified memory run the 12B model smoothly and can stretch to the 27B with slightly longer response times.

My recommendation: run the 12B model for autocomplete and keep the 27B loaded for chat tasks. Continue lets you configure different models for different features, so you can get the speed of the smaller model where it matters and the quality of the larger one where you need it.

Gemma 4 vs GitHub Copilot: What I Actually Found After a Month

This is the comparison that matters, so here is the honest version based on running both tools side by side on the same projects for several weeks.

Feature	Gemma 4 (Local)	GitHub Copilot
Inline autocomplete quality	Good for common patterns, occasionally misses context	Excellent — reads your project structure
Autocomplete speed	Depends on your GPU (300ms–2s)	Consistently fast (100–300ms)
Project context awareness	Limited to active file and whatever you paste into chat	Indexes your workspace and open tabs
Chat and code explanation	Strong with the 27B model	Very strong via Copilot Chat
Test generation	Covers main paths, sometimes misses edge cases	Better edge case coverage overall
Language support	Strong in popular languages (Python, JS, TS, Go, Dart)	Strong across almost everything, including niche languages
Privacy	Fully local — your code never leaves your machine	Cloud-based — code sent to GitHub/Microsoft servers
Offline availability	Yes — works without any internet connection	No — requires an active connection
Cost	Free forever	$19/month individual, $39/month business
Setup effort	Moderate — install Ollama, pull model, configure extension	Minimal — install extension, sign in with GitHub

Who Should Use Gemma 4 and Who Should Stick With Copilot

Gemma 4 is the right choice when:

• Your company prohibits sending code to external servers. This is standard in finance, defence, healthcare, and any organisation with strict IP or compliance policies. Gemma 4 is the only serious option that keeps everything on-premises.
• You regularly code offline — on flights, in restricted networks, or in areas with patchy internet. Gemma 4 works identically with no connection at all.
• You are a student, freelancer, or early-career developer who would rather not spend $228 a year on a coding assistant. Gemma 4 gives you a capable alternative at zero ongoing cost.
• You want hands-on experience with local AI infrastructure. Running models locally is an increasingly relevant skill in production engineering, and this is a low-stakes way to build that expertise.
• Your primary use case is chat-based — explaining code, refactoring, debugging. The quality gap between Gemma 4 and Copilot is much narrower for these tasks than it is for inline autocomplete.

Copilot is still worth the subscription when:

• Fast, accurate inline autocomplete is your top priority. Copilot is still meaningfully better at predicting your next line of code, especially in large codebases with complex interdependencies.
• You work across many languages and frameworks, including newer or less common ones. Copilot's cloud-based models have broader training coverage.
• You want a zero-configuration experience. Install the extension, sign in, done. No GPU requirements, no model downloads, no troubleshooting driver issues.
• Your team already has a Copilot Business or Enterprise licence. At that point, the cost is covered and the team-level features (audit logs, policy controls, organisational context) add genuine value.

For a wider comparison across all AI tools, see my full AI model comparison for 2026. And for specific tasks where the free model outperforms paid alternatives, read 5 real-world tasks where Gemma 4 beats paid models.

Frequently Asked Questions

What hardware do I need to run Gemma 4 as a coding assistant in VS Code?

A GPU with 8GB of VRAM gives you a comfortable experience with the Gemma 4 12B model — that covers cards like the RTX 3060, RTX 4060 Ti, or RTX 4070. For the more capable 27B model, you need 16GB or more. If you are on CPU only, the 4B model works but completions take 2–5 seconds instead of under a second, which makes inline autocomplete frustrating. Apple Silicon Macs with 16GB unified memory handle the 12B model well through Ollama, and 24GB opens up the 27B.

Is Gemma 4 in VS Code as good as GitHub Copilot?

Not quite for inline autocomplete — Copilot's suggestions are faster, more contextually aware across your workspace, and handle a wider range of languages. Where Gemma 4 closes the gap significantly is in chat-based workflows: explaining code, refactoring, debugging from stack traces, and generating test scaffolds. The real advantages of Gemma 4 are privacy (your code stays on your machine), cost (completely free), and offline access. For developers whose company policies prohibit cloud-based tools or who want a capable assistant without the subscription, Gemma 4 is a genuinely strong option.

Can I use Gemma 4 and GitHub Copilot together in VS Code?

Absolutely, and this is actually my recommended setup for developers who have access to both. Use Copilot for inline tab-completions where speed and project context matter most, and use Gemma 4 through Continue for chat-based tasks where you want your code to stay local — explaining sensitive codebases, refactoring proprietary algorithms, having architectural discussions you would rather not send to a cloud API. You get the best of both worlds without them interfering with each other.

Which Gemma 4 model size is best for coding in VS Code?

Start with the 12B model — it gives you sub-second completions on a decent GPU, handles Python, JavaScript, TypeScript, Dart, and most popular languages well, and produces solid code for everyday tasks. Move up to the 27B if you find yourself hitting quality limitations on complex logic, architecture questions, or less common languages. The 4B model is fine for simple completions and quick chat questions but noticeably weaker on anything requiring multi-step reasoning. The 31B is only worth it if you have 24GB+ VRAM and primarily use the chat interface for deep code analysis.

Can I use Gemma 4 from the terminal to analyse my project files without VS Code?

Yes. If Ollama is installed and running, you can pipe any file directly to Gemma 4 from PowerShell or any terminal. For example: Get-Content .\app.py -Raw | ollama run gemma4 "Explain this code and find bugs". You can also concatenate multiple files, set system prompts with --system, or use a dedicated tool like Aider (pip install aider-chat) for full repo-aware AI pair programming from the command line. No VS Code or any editor extension required.

Can I run Gemma 4 locally inside GitHub Copilot Chat in VS Code?

Yes. Recent versions of the GitHub Copilot Chat extension support adding locally hosted Ollama models directly in the model picker. Open Copilot Chat, click the model dropdown at the bottom of the panel, choose Manage Models, select Ollama as the provider, and tick gemma4 from the list of models you have pulled. Once selected, Copilot Chat, inline chat (Ctrl+I), and Edits all route through your local Gemma 4 model instead of GitHub's servers. One caveat: ghost-text tab autocomplete still uses the cloud Copilot model — for local inline autocomplete, pair this with the Continue extension (Method 1).

Sources and Further Reading

• Google Gemma official documentation
• Ollama
• Continue — open-source AI code assistant
• GitHub Copilot
• Aider — AI pair programming in your terminal

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