How much VRAM does Gemma 3 12B need?

Gemma 3 12B requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Gemma 3 12B on a consumer GPU?

Yes, Gemma 3 12B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 12 GB GPU. For best performance, use a 16 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Gemma 3 12B's context window?

Gemma 3 12B supports a maximum context window of 32,768 tokens. The default context is 8,192 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Gemma 3 12B?

For Gemma 3 12B, Q4_K_M is the recommended quantization — it provides an excellent quality-to-VRAM ratio at ~0.0 GB. Q8_0 offers slightly better quality at ~2× the VRAM. Q4_K_M is the sweet spot for most users.

How does Gemma 3 12B compare to similar models?

Gemma 3 12B is a 12.0B Dense model from the Gemma family. Browse other Gemma models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

GemmaDenseGemma License (Apache 2.0 compatible)

Gemma 3 12B

12.0B

Parameters

32K

Max Context

Dense

Architecture

Mar 12, 2025

Released

Text + Vision

Modality

About Gemma 3 12B

Gemma 3 12B is Google's strongest mid-size open model. At 12B parameters with vision support, 32K context, and strong multilingual performance across 140+ languages, it delivers exceptional quality for its size. Uses GeGLU activation (unlike most models using SwiGLU) and a unique head_dim=160 design. At ~6.5 GB VRAM at Q4_K_M it fits on 8 GB GPUs. The Gemini-derived training recipe gives it strong instruction following and safety alignment.

General PurposeMultilingualVisionMid-Range GPU

Technical Specifications

Total Parameters12.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 3,840

Transformer Layers48

Attention Heads24

KV Headsn_kv = 6

Head Dimensiond_head = 160

Activation FunctionGeGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	32K ctx
Q4_K_M0.50 B/W ~97% of FP16	6.38Consumer GPU	11.83Consumer GPU
Q8_01.00 B/W ~100% of FP16	12.58Consumer GPU	18.03Consumer GPU
F162.00 B/W Reference	24.99Datacenter GPU	30.44Datacenter GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Model Transparency

Scores how well-documented and reproducible Gemma 3 12B is across 12 provenance dimensions. Higher is more transparent.

Param Density75

Tokenizer75

Dataset72

Training72

Reproducibility77

Upstream76

Architecture78

Versioning72

Hardware83

Downstream73

License84

Identity76

85+ Transparent

70+ Adequate

<70 Opaque

Benchmark Scores

MMLU76.8

HumanEval74.5

GSM8K87

BBH79.5

IFEval80

Arena Elo1210

Sources: Open LLM Leaderboard, LMSYS Chatbot Arena, model technical reports.

Quick Reference

Total Params12.0B

Context Window32K

ArchitectureDense

Layers48

Hidden Dim3840

LicenseGemma License (Apache 2.0 compatible)

VRAM Calculator

Layers

Context

256K

3.8B active

Find the right GPU for Gemma 3 12B

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide