PC for Gemma 4 12B: the ideal workstation
Gemma 4 12B is arguably the best-balanced local model currently available: multimodal, 256,000 token context, Apache 2.0 license, and above all a memory footprint that comfortably fits on a 16 GB card. This is precisely the profile for which our CoreAI 16 station was designed.
This guide explains what Gemma 4 12B offers, the actual VRAM needed according to quantization, and why a well-configured 16 GB workstation is the most relevant choice to fully enjoy it.
Gemma 4 12B in brief
Gemma 4 12B is an open 12-billion-parameter model by Google DeepMind. It is the mid-tier member of the Gemma 4 family, between the small edge models (E4B) and the large 26B MoE model.
Native multimodal
Native text, image, video, and audio in a single transformer. It can process inputs like multi-minute video clips with synchronized audio.
256K token context
A massive context window, ideal for analyzing long documents, codebases, or full conversation histories.
Apache 2.0 License
Free commercial use, without restrictions. This is a major change from Gemma 3, whose conditions were more restrictive.
Multilingual
Supports over 140 languages, including excellent French proficiency, for both writing and comprehension.
How much VRAM for Gemma 4 12B?
This is where Gemma 4 12B shines: it is surprisingly lightweight for its quality. Here are the actual requirements according to quantization, for a short to medium context.
| Quantization | VRAM required | Quality | Recommendation |
|---|---|---|---|
| Q4_K_M | about 6.6 GB | Close to the original | The sensible defect |
| QAT Q4_0 | about 6.6 GB | Better than a classic Q4 | Best quality/size ratio |
| Q5_K_M | about 8 to 9 GB | Very high | If you have the margin |
| Q8_0 | about 13 GB | Maximum fidelity | Ideal on 16 GB |
Why 16 GB of VRAM is the ideal setup
Gemma 4 12B technically fits on 8 GB in Q4. But a 16 GB card changes everything in practice, for three reasons.
First, you can upgrade to Q8_0 (about 13 GB) for maximum fidelity, where an 8 GB card limits you to Q4.
Next, the 256K token context consumes a huge amount of memory beyond the model itself. With 8 GB, you quickly run out of space when working with long documents. With 16 GB, you keep plenty of margin for context.
Finally, 16 GB lets you run Gemma 4 12B and other tasks simultaneously: an embedding model for document search, or another model loaded at the same time.
Our recommendation: the CoreAI 16 workstation
The Radiance PC CoreAI 16 is perfectly sized for this profile. Its RTX 5060 Ti 16 GB card runs Gemma 4 12B in all quantizations, up to Q8, with a large context window, and remains upgradeable for the future.
Radiance PC CoreAI 16 — RTX 5060 Ti 16 GB
- GPU NVIDIA RTX 5060 Ti 16 GB GDDR7
- CPU AMD Ryzen 5 7500F
- RAM DDR5 16 GB, upgradeable
- Storage NVMe 1 TB
- OS Windows 11 Pro or Ubuntu
- Format Compact and quiet tower
Gemma 4 12B in Q8 with large context, and room for 14B models and beyond.
Run Gemma 4 12B locally
The easiest way is through Ollama. A few commands are enough.
# Standard version ollama run gemma4:12b # QAT version (better quality at the same size) ollama run gemma4:12b-qat # For a large context, create a dedicated variant: cat > Modelfile <<'EOF' FROM gemma4:12b-qat PARAMETER num_ctx 65536 EOF ollama create gemma4-12b-ctx -f Modelfile
Gemma 4 12B also works very well with LM Studio (built-in model browser) and llama.cpp. On our machines, everything is preconfigured on request.
What if your needs evolve?
Gemma 4 12B is an excellent starting point. If you want to run larger models later, here are our other stations, from the same workshop in Auriol (13390).
In brief
What VRAM for Gemma 4 12B?
6.6 GB in Q4 is enough to get started. 16 GB allows Q8, a large context, and some margin. This is the recommended setup for serious use.
Q4, QAT Q4_0, or Q8?
The QAT Q4_0 offers the best quality/size ratio (same footprint as classic Q4, better accuracy). Q8 is reserved for maximum fidelity, ideal on 16 GB.
Is Gemma 4 12B free?
Yes, Apache 2.0 license, free commercial use included. You only pay for the hardware.
Can it process images, video, and audio?
Yes, it is a native multimodal model: text, image, video, and audio all in one interface.
Which machine to buy?
The CoreAI 16 (RTX 5060 Ti 16 GB, starting at €1,703) is precisely sized for Gemma 4 12B, delivered ready to use.
