Step-by-step guides for running open AI models like Google's Gemma 4 on your own hardware — no cloud bills, no complexity.
Free Tools
Three interactive tools built on the same verified numbers as our guides — get your answer in seconds, then read deeper only if you want to.
Pick your Mac, GPU, or PC and get an instant verdict for every Gemma 4 model — plus a dedicated page for 44 popular device configurations.
Check your deviceThe exact Ollama, llama.cpp, or LM Studio command for any model size and quant — copied straight from our verified setup guides.
Get your commandTell it what you want to do and what hardware you have — get the right Gemma 4 size and quant back, with benchmark-backed reasoning.
Find your modelStart Here
24 guides, sorted by hardware. Pick the one that matches what you're actually sitting in front of.
Apple Silicon's unified memory is the easiest way to run Gemma 4 — which model fits depends on your RAM tier and which Mac you own.
VRAM capacity, not raw horsepower, decides which Gemma 4 model actually fits and how fast it runs on your card.
You're not locked out — every Gemma 4 variant runs on CPU alone, and the 26B MoE model is a surprisingly capable pick.
Pick your exact device and get an instant verdict for every Gemma 4 model — no reading required.
One reference guide covering all five Gemma 4 models, their real RAM/VRAM needs, and which Mac or GPU runs each.
Latest Guides
Verified, hands-on tutorials for running Gemma 4 on real hardware — Macs, GPUs, mini PCs, and phones — updated as new devices and models land.
The complete math behind our Gemma 4 hardware checker: weight size from parameters, why usable memory isn't total memory, the comfortable/tight thresholds, and where the model is honestly incomplete.
A decision framework for choosing a Gemma 4 quantization level — what quantization trades away, why official QAT beats plain Q4_0, and why the 26B A4B MoE model is the one exception.
Yes, Ollama runs every Gemma 4 model. Exact ollama run gemma4 commands for E4B, 12B, 26B and 31B, RAM needs per size, image input, and API examples.
Gemma 4 with no GPU: which models fit in 8-64GB RAM, real tokens/sec, and why the MoE 26B model is the CPU sleeper pick.
Can the RTX 3060 run Gemma 4? Yes — 12B fits comfortably in 12GB. Real VRAM numbers, tokens/sec, CPU offload, and the 8GB variant caveat.
RTX 3070, 3070 Ti, 3080, and 3080 Ti tested against Gemma 4's memory needs. Faster than a 3060 — but 8GB is more limiting than you'd expect.
Why Gemma 4
Gemma 4 packs state-of-the-art multimodal capabilities into a size that actually runs on your laptop.
Gemma 4 runs vision + text natively on your local GPU or Apple Silicon — no API keys, no latency, total privacy.
The 4B variant runs at 40+ tokens/second on M2 MacBook Air. No spinning up cloud VMs — just instant results.
The smallest model supports 128K tokens; 12B, 26B MoE, and 31B all extend to 256K — enough for entire codebases or long documents in a single prompt.
Once downloaded, Gemma 4 works entirely offline. Perfect for air-gapped environments, travel, or sensitive workloads.
Ollama exposes a local REST endpoint. Swap cloud LLM APIs for Gemma 4 in your apps with a one-line URL change.
Gemma 4 is free for commercial use. Build, ship, and monetize your AI product without royalty headaches.
Model Selection Guide
The two strongest open model families in 2026, compared head-to-head at every size you can run locally.
| Model | Params | Context | Input ➔ Output | Min RAM | License | Intended Platform |
|---|---|---|---|---|---|---|
Gemma 4 E2B | 2B | 128K | Text, images, audio → Text | 4 GB | Apache 2.0 | Mobile devices |
Gemma 4 E4B | 4B | 128K | Text, images, audio → Text | 6 GB | Apache 2.0 | Mobile devices and laptops |
Gemma 4 12B | 12B | 256K | Text, images, audio → Text | 8 GB | Apache 2.0 | 16 GB laptops, Macs, and desktop GPUs |
Gemma 4 26B A4B | 26B (4B active) | 256K | Text, images → Text | 16 GB | Apache 2.0 | Desktop computers and small servers |
Gemma 4 31B | 31B | 256K | Text, images → Text | 20 GB | Apache 2.0 | Large servers or server clusters |
| Qwen Models | ||||||
Qwen2.5-VL 3B | 3B | 32K | Text, images → Text | ~4 GB | Apache 2.0 | Mobile devices and laptops |
Qwen 3.5 4B | 4B | 262K | Text, images → Text | ~4 GB | Apache 2.0 | Laptops and desktops |
Qwen 3.5 35B-A3B | 35B (3B active) | 262K | Text, images → Text | ~20 GB | Apache 2.0 | Desktops and small servers |
Qwen 3.5 27B | 27B | 262K | Text, images → Text | ~17 GB | Apache 2.0 | Workstations and servers |
* Gemma 4: Google AI documentation. Qwen 3.5: Hugging Face model cards (262K native context; YaRN extension per README). Qwen2.5-VL: 32K default in config. Min RAM ≈ typical 4-bit local load; actual use depends on context and framework.
Full Gemma 4 vs Qwen 3.5 benchmark analysis →Real-world Applications
From solo productivity to multiplayer experiences — Gemma 4 unlocks a new class of privacy-first, offline-capable apps.
Load your textbooks as PDFs, then ask Gemma 4 to explain, quiz, and summarize — entirely on-device. Works on planes, in libraries, anywhere without Wi-Fi.
Run Gemma 4's vision model on your home server to power live trivia, image-based guessing games, or creative storytelling — all processed locally, no latency.
Point Gemma 4 at your codebase via the OpenAI-compatible API. Get instant PR reviews, bug explanations, and refactor suggestions — without sending code to any server.
All Guides
Whether you're checking hardware, running your first model, or setting up a coding assistant — we've got you covered.
From zero to running Gemma 4 on your own hardware — pick a setup path.
Data-driven deep dives into Gemma 4 performance and capabilities.
Configure Gemma 4 as your personal AI coding assistant on Apple Silicon.
