Open-weight local LLM
Gemma 4 12B
Google DeepMind 12B unified multimodal model. Text, image, audio and video inputs, 256K context, Apache 2.0, and a strong local sweet spot for 16-32 GB machines.
16 GB sweet spot
16 GB RAM
Q4_K_M
Private multimodal assistant
Parameters
12B
Minimum RAM
16 GB
Model size
8.2 GB
Quantization
Q4_K_M
Can Gemma 4 12B run locally?
Gemma 4 12B is a practical pick for 16 GB machines, especially with Q4_K_M quantization.
Search for gemma-4-12b-it in LM Studio or another GGUF-compatible runtime.
Model source
lmstudio-community/gemma-4-12B-it-GGUFchatvisionaudiocodereasoningpowermultimodalgeneral
Install path
01
Check RAM fitMinimum 16 GB RAM. Start with the Q4_K_M quant.02
Load the modelSearch gemma-4-12b-it in LM Studio.03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.Strengths
- 12B local sweet spot
- Unified text, image, audio and video input
- 256K context window
- Apache 2.0 license
- Stronger ceiling than edge-size Gemma 4 variants
Limitations
- Use the instruction-tuned variant for chat workflows
- Very long context increases memory use
- Runtime support may arrive at different speeds across LM Studio, Ollama, MLX and llama.cpp
Best use cases
- Private multimodal assistant
- Screenshot and document analysis
- Local coding and reasoning
- Long-context research on 32 GB machines
Capability profile
Technical notes
This model fits these next steps
Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.