Gemma 4 (12B)
Google's latest open-weight Gemma generation for on-device and single-GPU inference
Model Summary
Family
Gemma
Version
4.0
Parameters
12B (est.)
Parameter counts for closed models are estimates; vendors rarely publish exact sizes.
VRAM Requirements by Quantization
Memory needed to serve Gemma 4 (12B) for inference, including a 20% overhead for activations and KV cache.
| Precision | VRAM needed | Smallest single GPU that fits |
|---|---|---|
| INT4 (4-bit) | 6.71 GB | NVIDIA P100 SXM2 (16 GB) |
| INT8 (8-bit) | 13.41 GB | NVIDIA P100 SXM2 (16 GB) |
| FP16 (16-bit) | 26.82 GB | NVIDIA V100 SXM2 32GB (32 GB) |
| FP32 (32-bit) | 53.64 GB | NVIDIA H100 SXM5 (80 GB) |
Recommended GPU Configurations
Cheapest on-demand configurations to serve Gemma 4 (12B) at 8-bit (13 GB VRAM).
1x NVIDIA T4
16 GB total VRAM · Turing
~$0.50/h
1x NVIDIA P100 SXM2
16 GB total VRAM · Pascal
~$0.60/h
1x NVIDIA L4
24 GB total VRAM · Ada Lovelace
~$1.00/h
Quick GPU Planning
Use the calculator pre-filled with this exact version to estimate memory, speed, and compute requirements in a few clicks.
Access Pre-filled CalculatorFrequently Asked Questions
How much VRAM do you need to run Gemma 4 (12B)?
With an estimated 12B parameters, Gemma 4 (12B) needs roughly 13 GB of VRAM in 8-bit (INT8), 7 GB in 4-bit, and 27 GB in FP16, including a 20% overhead for activations and KV cache.
Which GPUs can run Gemma 4 (12B)?
At 8-bit quantization, the most cost-effective option is 1x NVIDIA T4 (16 GB combined VRAM, around $0.50/hour on-demand). Higher-end cards like the NVIDIA B200 or AMD MI355X reduce the GPU count needed.
Can Gemma 4 (12B) run on a single GPU?
Yes. In 8-bit, a single NVIDIA P100 SXM2 (16 GB) fits the model.
How much does it cost to serve Gemma 4 (12B) in the cloud?
Renting 1x T4 costs on the order of $0.50/hour, i.e. about $365/month running 24/7. Actual prices vary by provider and commitment; spot and reserved capacity can be significantly cheaper.
Deploy on a GPU cloud
Rent 1x T4 by the hour instead of buying hardware.
