Parameters
27B
Context Length
128K
Modality
Multimodal
Architecture
Dense
License
Gemma Terms of Use
Release Date
12 Mar 2025
Knowledge Cutoff
Aug 2024
Attention Structure
Grouped-Query Attention
Hidden Dimension Size
4096
Number of Layers
46
Attention Heads
64
Key-Value Heads
16
Activation Function
-
Normalization
RMS Normalization
Position Embedding
ROPE
VRAM requirements for different quantization methods and context sizes
Gemma 3 is a family of lightweight, state-of-the-art models developed by Google DeepMind, designed with research and technology derived from the Gemini models. The Gemma 3 27B variant is a multimodal model engineered to process both textual and image inputs, generating text-based outputs. This model variant is intended for broad application across various generation tasks, including question answering, summarization, and complex reasoning, and supports over 140 languages. Its design focuses on enabling deployment on a range of hardware, from consumer-grade devices like laptops and workstations to specialized cloud infrastructure.
Gemma 3 is a family of open, lightweight models from Google. It introduces multimodal image and text processing, supports over 140 languages, and features extended context windows up to 128K tokens. Models are available in multiple parameter sizes for diverse applications.
Ranking is for Local LLMs.
Rank
#32
Benchmark | Score | Rank |
---|---|---|
Summarization ProLLM Summarization | 0.8 | 4 |
StackEval ProLLM Stack Eval | 0.91 | 5 |
StackUnseen ProLLM Stack Unseen | 0.37 | 7 |
QA Assistant ProLLM QA Assistant | 0.91 | 9 |
Professional Knowledge MMLU Pro | 0.68 | 14 |
Agentic Coding LiveBench Agentic | 0.03 | 17 |
Mathematics LiveBench Mathematics | 0.52 | 17 |
Coding LiveBench Coding | 0.49 | 18 |
Reasoning LiveBench Reasoning | 0.34 | 20 |
Data Analysis LiveBench Data Analysis | 0.51 | 20 |
Graduate-Level QA GPQA | 0.42 | 21 |
General Knowledge MMLU | 0.42 | 28 |
Overall Rank
#32
Coding Rank
#23
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Choose the quantization method for model weights
Context Size: 1,024 tokens