Google DeepMind FP16 12B Parameters

VRAM Requirements for
Gemma 3 12B FP16

To run Gemma 3 12B locally at FP16 quantization, you need at minimum 29.6 GB of GPU VRAM.

29.6 GB
Required VRAM
24.44 GB
File Size
131K tokens
Context Window
12B
Parameters
Estimated VRAM Required
29.6
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (FP16) 24.44 GB
Context Overhead (131,072 tokens × 12B × 2 ÷ 1M) 3.146 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 29.6 GB

Try a Different Quantization

Use the interactive calculator to compare Gemma 3 12B across all available formats.

Open Live Calculator →

Gemma 3 12B — Other Quantizations

Q4_K_M 7.27 GB file
12.4 GB
Q8_0 12.98 GB file
18.2 GB
FP16 Current
29.6 GB
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Frequently Asked Questions

Can I run Gemma 3 12B FP16 on a consumer GPU?
Running Gemma 3 12B FP16 locally requires 29.6 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Gemma 3 12B?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is FP16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.