Gemma 4 12B
Parameters
11.95B
Context Length
262K
Modality
Multimodal
Auxiliary Parameters
550M
Architecture
Dense
KV Cache Rate
384 KB/tok
License
Apache-2.0
Release Date
3 Jun 2026
Knowledge Cutoff
-
API Pricing (per 1M)
Input: $0.10 · Output: $0.30
Hardware Sizing
Precision
Context
Workload Memory: 11.06 GB
INT4 · 8K context
Base Weights: 5.56 GB
KV Cache: 3.00 GB
Runtime & Graphs: 2.50 GB
Minimum Required Hardware
1x NVIDIA L4 24GB
11.1 / 21.6 GB usable
1x RTX 4060 Ti 16GB
11.1 / 14.4 GB usable
Apple M4 24GB
11.1 / 18.0 GB usable
Determine requirements for your specific workload:
Open in VRAM Calculator
Architecture Diagram
Evaluation Benchmarks
Rank
#135
| Benchmark | Score | Rank |
|---|---|---|
Professional Knowledge | 0.772 | 34 |
Graduate-Level QA | 0.788 | 68 |
Coding Index | 0.31 | 118 |
Intelligence Index | auto 0.14 Standard 0.09 | 206 244 |
Rankings
Overall Rank
#135
Coding Rank
#121
About Gemma 4 12B
Google DeepMind's 12B dense open-weights model released June 3, 2026, bridging the gap between the edge-friendly E4B and the more advanced 26B MoE. Uniquely features an encoder-free unified architecture that projects raw image patches and audio waveforms directly into the LLM embedding space through lightweight linear layers, eliminating the latency and memory overhead of separate encoders. Supports 256K token context, native text/image/audio inputs, configurable thinking mode, and runs on consumer laptops with 16GB of RAM.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
16
Key-Value Heads
8
Attention Head Dimension
256
Position Embedding
Absolute Position Embedding
RoPE Theta
10,000
Sliding Window Attention
Yes
Sliding Window Size
1,024
Sliding Window Ratio
83.3%
Normalization
RMS Normalization
Activation Function
GELU
Dimensions
Auxiliary Parameters
550M
Hidden Dimension Size
3,840
Number of Layers
48
FFN Intermediate Size (Dense)
15,360
Tokenizer
Vocabulary Size
262,144
About Gemma 4
Gemma 4 is Google DeepMind's most advanced open model family, built from Gemini 3 research and technology. Featuring both Dense and Mixture-of-Experts (MoE) architectures, these multimodal models handle text, images, and audio (on smaller variants), with context windows up to 256K tokens. Designed for frontier-level performance across reasoning, coding, and agentic workflows, Gemma 4 delivers unprecedented intelligence-per-parameter from mobile devices to enterprise servers. Released under Apache 2.0 license.