Your cloud AI coding subscription just became optional.
Gemma 4, released April 2, 2026 under a fully permissive Apache 2.0 license, runs entirely on your local hardware, costs nothing per month, and keeps every line of your code off external servers. For developers paying $10–$100/month for Copilot, Cursor, or Claude Code, that’s a real alternative — not a compromise.
This guide walks you through the complete setup pipeline for a Gemma 4 local AI coding assistant: choosing the right model variant for your hardware, installing Ollama, selecting the right quantization settings, and connecting Gemma 4 to VS Code or JetBrains via the Continue extension. You’ll also get honest performance benchmarks — including one hardware trap that tanks speed on an otherwise excellent model.
Why Developers Are Ditching Cloud AI Coding Tools
The numbers tell a clear story. According to the Stack Overflow 2025 Developer Survey (49,000+ respondents), 81% of developers cite security and privacy as their #1 concern when evaluating AI coding tools — ranking above cost and availability of better alternatives. It’s not a niche worry; it’s the single biggest barrier to adoption.
For enterprise teams, the stakes are even higher. 61% of developers at large enterprises (1,000+ employees) are specifically worried about exposing sensitive company or customer data to cloud AI tools. GDPR requirements, compliance frameworks, and air-gapped deployment environments make cloud-based AI essentially off-limits for entire categories of professional work.
Then there’s the subscription math. GitHub Copilot runs $10–19/month per seat. Cursor charges $20–40/month. Claude Code Pro can approach $100/month at heavy usage. Multiply that across a team and you’re looking at a significant recurring line item for tooling that also comes with rate limits and availability dependencies.
Local AI solves all three problems simultaneously. Your code never leaves your machine. There are no usage caps. And after the one-time hardware cost, the marginal cost per query is zero.
Gemma 4 in 60 Seconds: What Makes It Different From Every Other Local Model
Gemma 4 isn’t an incremental update. Google DeepMind released it on April 2, 2026, and the benchmarks represent a genuine capability leap from its predecessor.
On the Codeforces ELO leaderboard, Gemma 3 27B scored 110. Gemma 4 31B scored 2,150 — placing it third among all open models. On AIME 2026 (a rigorous math reasoning benchmark), Gemma 4 31B scored 89.2%, up from 20.8% for Gemma 3 27B. The 26B MoE variant scored 88.3% with only 3.8 billion active parameters per forward pass. These aren’t marginal improvements.
The licensing story matters for professional use. Previous Gemma versions included non-compete clauses that made enterprise adoption legally uncertain. Gemma 4 ships under Apache 2.0: fully open, commercially usable, no restrictions. If you’re building a product or working in a regulated industry, that clarity removes a real obstacle.
Gemma 4 also introduces native multimodal support (vision + text input). Audio support has known caveats in current local inference builds. There’s also a documented `–image-min-tokens` bug in recent llama.cpp versions that can cause crashes when multimodal flags are enabled — disable image processing flags until a patch lands.
Which Gemma 4 Model Should You Run?
This is where most setup guides set you up for a disappointing first experience. Recommending “the 31B model” without hardware context can turn a 2-second response into a 38-second wait.
The four sizes explained
Gemma 4 ships in four variants:
- E2B / E4B — Edge models for laptops and low-power devices with 4–8GB VRAM. Fast but limited for complex coding tasks.
- 26B MoE (Mixture of Experts) — The best balance of speed and quality for local coding. Only 3.8B parameters are active per forward pass, making it dramatically more efficient than its parameter count suggests.
- 31B Dense — Highest quality ceiling, but requires careful hardware matching. All 31B parameters activate on every forward pass.
The hardware decision that changes everything
Here’s the benchmark data most guides skip entirely. On an NVIDIA RTX 4090 (24GB VRAM), the Gemma 4 26B MoE model achieves 149 tokens/second generation. The 31B Dense model on the same GPU achieves 7.84 tokens/second — a 19x slowdown — because the 31B model’s weights spill over from VRAM into system RAM, which bottlenecks throughput catastrophically.
To put that in concrete terms: at 149 t/s, a 300-token code explanation completes in about 2 seconds. At 7.84 t/s, it takes nearly 40 seconds. One feels like Copilot. One feels like a slow API call.
The spillover problem is so severe that running the 31B Dense CPU-only on a 64-core AMD Threadripper with 256GB RAM achieves 8.80 t/s — marginally faster than the GPU-accelerated setup — because there’s no cross-memory bandwidth bottleneck at all.
Choosing by hardware
| Hardware | Recommended Model | Expected Speed |
|—|—|—|
| RTX 3060 12GB | 26B MoE, Q4_K_M | ~40–60 t/s |
| RTX 4090 24GB | 26B MoE, Q4_K_M | ~149 t/s |
| M-series Mac 16GB | 12B or 26B MoE | ~25–40 t/s |
| MacBook Pro M4 Pro 48GB | 26B MoE | ~51 t/s |
| Multi-GPU / 40GB+ VRAM | 31B Dense, Q4_K_M | Full speed |
Unless you have multiple GPUs or a single card with 32GB+ VRAM, choose the 26B MoE. It’s faster and nearly as capable for everyday coding tasks.
Step 1 — Install Ollama and Pull Your Model
Ollama is the fastest path to running Gemma 4 locally. It handles model downloads, manages quantization, and exposes a local API that tools like Continue.dev can connect to directly.
Install Ollama
macOS / Linux:
“`bash
curl -fsSL https://ollama.com/install.sh | sh
“`
Windows: Download the installer from ollama.com. It runs as a background service and auto-starts on login.
Pull and run your model
“`bash
# 26B MoE — recommended for most hardware
ollama pull gemma4:26b-moe
# 31B Dense — only if you have 32GB+ VRAM
ollama pull gemma4:31b
# Start a session
ollama run gemma4:26b-moe
“`
Ollama defaults to Q4_K_M quantization, which is the sweet spot for most setups. The model downloads once and runs instantly on subsequent calls. Once running, Ollama exposes a local API at `http://localhost:11434` — this is the endpoint Continue.dev will connect to in the next step.
LM Studio is a capable GUI alternative if you prefer a visual interface. Its new headless CLI also supports Claude Code-style agentic workflows with Gemma 4, making it useful beyond basic chat.
Step 2 — Quantization Explained: Choosing Q4_K_M vs. Q8 vs. UD-Q4_K_XL
Quantization reduces the numerical precision of model weights to shrink memory requirements. The tradeoffs are real but manageable — and choosing wrong either wastes VRAM or noticeably degrades output quality.
Your three practical options
Q4_K_M is the recommended default for most developers. For Gemma 4 31B, it requires ~20GB RAM/VRAM. For the 26B MoE, ~18GB. Quality loss compared to full precision is minimal for coding tasks, and Ollama uses this automatically.
Q8 gives near-lossless quality but nearly doubles the memory requirement: ~34GB for the 31B model, ~28GB for the 26B MoE. Only practical with workstation hardware or multi-GPU setups.
UD-Q4_K_XL (available via Unsloth) is a newer quantization format that achieves noticeably better quality than standard Q4_K_M at a similar memory footprint. If you’re chasing maximum output quality on constrained hardware, this format is worth testing.
Fine-grained control with llama.cpp
Ollama abstracts GPU layer management, which is convenient but limits control. llama.cpp lets you specify exactly how many transformer layers to offload to GPU:
“`bash
./llama-cli -m gemma4-26b-moe-q4_k_m.gguf \
–n-gpu-layers 40 \
-p “Refactor this Python function to use async/await:”
“`
Tune `–n-gpu-layers` based on your available VRAM. Start high and lower it if you encounter out-of-memory errors. For most developers, Ollama’s defaults are the right starting point — reach for llama.cpp when you need to squeeze out extra performance or debug specific hardware configurations.
Step 3 — Connect Gemma 4 to VS Code or JetBrains via the Continue Extension
Running Gemma 4 in a terminal is useful. Running it inside your IDE — with tab autocomplete and inline chat — is what replaces Copilot.
Continue.dev is the best open-source option available. It supports chat, inline editing, tab autocomplete, and critically, Fill-in-the-Middle (FIM) — the completion mode that predicts what belongs between your cursor and the next line of code. FIM is what makes tab completion feel responsive and context-aware rather than like a slow chatbot bolted onto your editor.
Install and configure Continue
- Install the Continue extension from the VS Code Marketplace (or the JetBrains Plugin Repository).
- Open your Continue config file at `~/.continue/config.json`.
- Add Gemma 4 as your model, pointing to your local Ollama instance:
“`json
{
“models”: [
{
“title”: “Gemma 4 26B MoE”,
“provider”: “ollama”,
“model”: “gemma4:26b-moe”,
“apiBase”: “http://localhost:11434”
}
],
“tabAutocompleteModel”: {
“title”: “Gemma 4 Autocomplete”,
“provider”: “ollama”,
“model”: “gemma4:26b-moe”
}
}
“`
- Restart VS Code. Continue appears in the left sidebar.
Tab autocomplete activates as you type. Inline chat opens with `Ctrl+I` (or `Cmd+I` on Mac). The sidebar chat panel handles longer refactoring sessions, code explanations, and test generation — the tasks where Gemma 4’s reasoning improvements show up most clearly.
Real-World Coding Benchmarks: What Gemma 4 Does Well (and Where It Still Falls Short)
The speed numbers above describe inference performance. Here’s how that translates to practical coding tasks.
Where Gemma 4 closes the gap
Refactoring and code explanation are genuine strengths at local speeds. Ask it to explain a complex function, suggest a pattern refactor, or describe what an unfamiliar codebase does — responses at 26B MoE speeds are fast and accurate enough to fit naturally into a coding workflow.
Test generation is another competitive area. Gemma 4’s Codeforces ELO of 2,150 reflects real improvements in code structure reasoning, particularly for algorithmic problems and data manipulation tasks.
General reasoning quality also holds up: Gemma 4 31B’s MMLU Pro score of 85.2% exceeds Qwen 3.5 27B on the same benchmark — suggesting the improvements extend beyond narrow benchmarks into multi-step reasoning that coding tasks require.
Where cloud tools still have an edge
Inline tab-completion latency is the most noticeable gap. At 149 t/s locally, completions take 1–3 seconds. Cloud tools like Copilot return completions in under 500ms over a good connection. For fast typists, that gap is perceptible — though on slower network connections or high-latency cloud APIs, the advantage narrows.
Cross-file context awareness requires additional configuration. Copilot and Cursor have purpose-built codebase indexing baked in. Continue.dev supports codebase context through its `@codebase` command and embedding-based retrieval, but it requires setup and doesn’t match the seamlessness of proprietary tools out of the box.
For isolated tasks — refactoring a function, writing unit tests, explaining unfamiliar code — the practical gap between local Gemma 4 and cloud tools is small. For large-codebase navigation, plan to configure Continue’s indexing features.
The Full Cost Comparison: Is a Local AI Coding Assistant Worth It?
The math is direct.
| Tool | Monthly Cost |
|—|—|
| GitHub Copilot Individual | $10–19/month |
| Cursor Pro | $20–40/month |
| Claude Code Pro | ~$100/month |
| Gemma 4 Local | $0/month |
If you already own an RTX 3060 12GB or any GPU with 12GB+ VRAM, your hardware cost is paid. Gemma 4 runs today with zero additional spend.
If you need to buy hardware: a new RTX 3060 12GB runs around $300. At $19/month for Copilot Individual, that investment pays for itself in under 16 months — and continues generating savings indefinitely. Used cards often cut the payback period below 12 months.
For enterprise teams where cloud AI is blocked by policy, the ROI framing is secondary: a Gemma 4 local AI coding assistant isn’t competing on cost — it’s the only viable option. A local deployment across a team of 10 developers, each saving the 3.6 hours per week that AI tools deliver on average (Stack Overflow 2025), represents real productivity gains that would otherwise be lost.
The Apache 2.0 license removes the final blocker for commercial deployment. Embed it in a product, run it in a client environment, deploy it in an air-gapped data center — all fully permitted, no legal review required.
Your Next Step
Setting up a Gemma 4 local AI coding assistant takes about 20 minutes from zero. Install Ollama, pull the 26B MoE model, configure Continue.dev, and you’ll have a private, offline-capable coding assistant that costs nothing per month and keeps your code entirely on your machine.
The 26B MoE model on consumer hardware delivers the speed that makes IDE integration feel like a tool, not a chore. The quality improvements over Gemma 3 are large enough to matter for real coding work. And the Apache 2.0 license means there’s no asterisk on commercial use.
If your current AI coding workflow is constrained by privacy requirements, subscription costs, or unreliable cloud availability — this setup addresses all three.
Start with `ollama pull gemma4:26b-moe`. Your first free, fully private code completion is one command away.