Gemma 4 QAT on 10GB Laptop
Use Gemma 4 12B with QAT quantization to fit local inference into 6.7GB VRAM. Ideal for 10GB laptops and compact desktop GPUs.
Gemma 4 QAT on 10GB Laptop is a local AI stack for Run a powerful local model on a 10GB VRAM laptop. Use Gemma 4 12B with QAT quantization to fit local inference into 6.7GB VRAM. Ideal for 10GB laptops and compact desktop GPUs. It combines 3 components, is rated advanced, and takes about 25 minutes to set up. Expect around $1,000 in hardware and $0/month versus cloud.
- Cost
- ~$1,000
- $0/mo vs cloud
- Difficulty
- advanced
- Setup time
- ~25 min
- Use case
- Run a powerful local model on a 10GB VRAM laptop
Gemma 4 QAT on 10GB Laptop
This stack uses Ollama with Gemma 4 QAT to run a 12B model on a 10GB GPU laptop. It is built around the latest Gemma 4 QAT checkpoints that reduce memory use while keeping local inference quality high.
What you get
- Local Gemma 4 12B inference on 10GB VRAM hardware
- QAT compression that fits the model into ~6.7 GB VRAM
- A laptop-friendly private AI stack for writing, notes, and prompts
Architecture
| Component | Role |
|---|---|
| Ollama | Local model server and GPU runtime |
| Gemma 4 12B | Quantized local model for inference |
Prerequisites
- Laptop with at least 10 GB VRAM, such as radeon-rx-6700s
- Latest GPU drivers and Vulkan support
- Ollama installed locally
- Enough disk space for the model cache (~40 GB)
Setup
- Install Ollama.
brew install ollama- Pull the QAT model.
ollama pull gemma-4:12b --quantization qat- Start Ollama.
ollama serve- Confirm the model is loaded and using GPU.
ollama psIf ollama ps shows the model and GPU usage, your stack is ready.
Use it
- Personal writing with faster local completion
- Private research without sending queries to the cloud
- Compact local AI demos on 10GB-class laptops
Cost vs cloud
| Local | Cloud | |
|---|---|---|
| Monthly | $0 | $20+ |
| Hardware | $1000 once | $0 |
| Privacy | High | Low |
Troubleshooting
- Model won’t load → verify your GPU has Vulkan and enough free VRAM.
- Ollama falls back to CPU → check
ollama psand update drivers. - Slow inference → close background apps and use the QAT model, not the unquantized version.
Swap components
- Want a smaller local model? Use Qwen 3.5 9B.
- Prefer a Mac-native stack? Use Ollama Mac Metal AI.
Frequently asked
What is the Gemma 4 QAT on 10GB Laptop stack for?
Use Gemma 4 12B with QAT quantization to fit local inference into 6.7GB VRAM. Ideal for 10GB laptops and compact desktop GPUs. It is purpose-built for Run a powerful local model on a 10GB VRAM laptop and runs entirely on your own hardware.
How much does the Gemma 4 QAT on 10GB Laptop stack cost?
Gemma 4 QAT on 10GB Laptop costs around $1,000 in hardware up front and $0/month to run, since everything is self-hosted — no per-token or subscription fees versus a cloud equivalent.
How long does it take to set up Gemma 4 QAT on 10GB Laptop?
Plan for roughly 25 minutes. The stack is rated advanced.
What do I need to run Gemma 4 QAT on 10GB Laptop?
Gemma 4 QAT on 10GB Laptop is built from 1 tool(s), 1 model(s), 1 hardware item(s). Each is listed below with a link.