EmbeddingGemma 2
Parameters
2B
Context Length
8K
Modality
Text
Architecture
Dense
License
Apache 2.0
Release Date
14 Sept 2026
Knowledge Cutoff
-
API Pricing (per 1M)
Self-hosted only
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
8,192 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for EmbeddingGemma 2 available.
Rankings
Overall Rank
-
Coding Rank
-
About EmbeddingGemma 2
EmbeddingGemma 2 is a specialized text embedding model based on Google's Gemma 2 architecture. It is designed for high-performance semantic search, retrieval-augmented generation, and text similarity tasks.
Technical Specifications
Attention
Attention Structure
Single-Head Attention
Attention Heads
4
Key-Value Heads
2
Attention Head Dimension
256
Position Embedding
ROPE
RoPE Theta
10,000
Sliding Window Attention
Yes
Sliding Window Size
512
Sliding Window Ratio
83.3%
Linear Attention
No
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
Gated GELU
Dimensions
Auxiliary Parameters
-
Hidden Dimension Size
512
Number of Layers
24
FFN Intermediate Size (Dense)
2,048
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
262,144
Resources
About Gemma 2
Gemma 2 is Google's family of open large language models, offering 2B, 9B, and 27B parameter sizes. Built upon the Gemma architecture, it incorporates innovations such as interleaved local and global attention, logit soft-capping for training stability, and Grouped Query Attention for inference efficiency. The smaller models leverage knowledge distillation.