Vision
How to deploy gemma-3-12b-it on a GPU cloud
A 12.2B vision-language model that reads images alongside text. Full specs, license and use cases.
gemma-3-12b-it size and hardware requirements
12.2B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
27.2 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 22.7 GB | 27.2 GB | RTX A6000 | 1 | $0.330/hr |
| FP8 (quantized) | 11.4 GB | 13.6 GB | RTX 4080 | 1 | $0.158/hr |
| INT4 (quantized) | 5.7 GB | 6.8 GB | RTX 4070 Super | 1 | $0.110/hr |
How to run gemma-3-12b-it
Run gemma-3-12b-it with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve google/gemma-3-12b-it --tensor-parallel-size 1Run gemma-3-12b-it with Ollama
Verified against Ollama's own library listing.
ollama run gemma3:12bRun gemma-3-12b-it with GGUF quantizations
Prebuilt GGUF weights published at unsloth/gemma-3-12b-it-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/gemma-3-12b-it-GGUFDeploy gemma-3-12b-it on Aquanode
Aquanode has no one-click deploy template for gemma-3-12b-it; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX A6000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod