litert-community/embeddinggemma-2-text-270m-litert-lm

Main Model Card: google/embeddinggemma-2

EmbeddingGemma 2 Text 270M is a highly efficient, text-only variant of the EmbeddingGemma 2 model family, optimized for low-latency deployment on consumer hardware and edge devices via LiteRT. With a footprint of 270M parameters (combining a 130M transformer with a 140M embedder), this model delivers high-quality text representations. It supports over 100 languages, programming code, and features Matryoshka Representation Learning (MRL) for adjustable vector dimensions (down to 128d) to dramatically lower vector storage overhead. This variant is ideal for text-centric on-device applications, including document search, classification, clustering, and question answering.

Try EmbeddingGemma 2 with LiteRT-LM

Android	iOS	macOS	Web

Ready to integrate this into your product? Get started here.

Try EmbeddingGemma 2 with MediaPipe

MediaPipe uses EmbeddingGemma 2 and LiteRT-LM to enable high-level, cross-platform tasks which you can experience firsthand with the following interactive web demos:

• Semantic image search powered by MediaPipe Universal Embedder and Semantic Retriever task.
• Decision making powered by MediaPipe Decision Maker.

Model Specifications

	EmbeddingGemma 2 Text 270M	EmbeddingGemma 2 Text Vision 440M	EmbeddingGemma 2 740M
Supported Modalities	Text	Text, Images	Text, Images, Video, Audio
Parameters	Total: 270M Transformer: 130M Embeddings: 140M	Total: 440M Transformer: 130M Embeddings: 140M Vision Encoder: 170M	Total: 740M Transformer: 130M Embeddings: 140M Vision Encoder: 170M Audio Encoder: 300M
Quantization Scheme	Transformer:     int4 per-channel (QAT) Embeddings:     int4 per-channel (QAT)	Transformer:     int4 per-channel (QAT) Embeddings:     int4 per-channel (QAT) Vision Encoder:     int8 per-channel (QAT)	Transformer:     int4 per-channel (QAT) Embeddings:     int4 per-channel (QAT) Vision Encoder:     int8 per-channel (QAT) Audio Encoder:     mixed int2/int4/int8     per-channel (QAT)
Download Size (CPU/GPU file)	165 MB	388 MB	485 MB
On-Demand Modality Loading	-	Yes	Yes
Supported Input Sizes	Text tokens: 128, 256, 512,     1024, 2048, 8192	Text tokens: 128, 256, 512,     1024, 2048, 8192 Image soft tokens: 70, 140	Text tokens: 128, 256, 512,     1024, 2048, 8192 Image soft tokens: 70, 140 Audio soft tokens: 12
Supported Output Sizes	128 (512 bytes) 256 (1024 bytes) 512 (2048 bytes) 768 (3072 bytes)	128 (512 bytes) 256 (1024 bytes) 512 (2048 bytes) 768 (3072 bytes)	128 (512 bytes) 256 (1024 bytes) 512 (2048 bytes) 768 (3072 bytes)
Link	litert-community/embeddinggemma-2-text-270m-litert-lm	litert-community/embeddinggemma-2-text-vision-440m-litert-lm	litert-community/embeddinggemma-2-740m-litert-lm

Additional Notes:

• LiteRT-LM automatically scales and patchifies images of any size to fit EmbeddingGemma 2's vision encoder model.
• LiteRT-LM supports EmbeddingGemma 2's streamed audio allowing a variety of different audio lengths.

EmbeddingGemma 2 Performance on LiteRT-LM

The text performance was measured by loading and running the 128 text signature. The latency reported is the average of 5 iterations.

Memory was measured with each platform's native metrics and is not directly comparable across operating systems. CPU memory was measured using, rusage::ru_maxrss on Android, Linux and IOT, task_vm_info::phys_footprint on iOS and MacBook, and process_memory_counters::PrivateUsage on Windows. With the exception of task_vm_info::phys_footprint, accelerator (GPU/NPU/TPU) memory is not included. Memory measurements were taken from the second load which reads from loading caches. Memory usage on the first load may vary.

Android

Device	Backend	Text latency
Google Pixel 11 Pro	TPU	8.3 ms
S26 Ultra	CPU	27.1 ms
S26 Ultra	GPU	25.9 ms

Device	Backend	Text CPU memory
Google Pixel 11 Pro	TPU	112 MB
S26 Ultra	CPU	334 MB
S26 Ultra	GPU	333 MB

iOS

Device	Backend	Text latency
iPhone 18 Pro	CPU	41.8 ms
iPhone 18 Pro	GPU	11.6 ms

Device	Backend	Text CPU/GPU memory
iPhone 18 Pro	CPU	84 MB
iPhone 18 Pro	GPU	85 MB

Linux

Device	Backend	Text latency
Arm 2.3 & 2.8GHz	CPU	105 ms
NVIDIA GeForce RTX 4090	GPU	7.6 ms

Device	Backend	Text CPU memory
Arm 2.3 & 2.8GHz	CPU	310 MB
NVIDIA GeForce RTX 4090	GPU	528 MB

macOS

Device	Backend	Text latency
MacBook Pro M5	CPU	31.3 ms
MacBook Pro M5	GPU	9.5 ms

Device	Backend	Text CPU/GPU memory
MacBook Pro M5	CPU	165 MB
MacBook Pro M5	GPU	233 MB

Windows

Device	Backend	Text latency
Dell XPS 16 (Intel Core Ultra Series 3)	CPU	71.7 ms
Dell XPS 16 (Intel Core Ultra Series 3)	GPU	19.2 ms
Dell XPS 16 (Intel Core Ultra Series 3)	Running Intel OpenVINO NPU	13.3 ms

Device	Backend	Text CPU memory
Dell XPS 16 (Intel Core Ultra Series 3)	CPU	211 MB
Dell XPS 16 (Intel Core Ultra Series 3)	GPU	822 MB
Dell XPS 16 (Intel Core Ultra Series 3)	Running Intel OpenVINO NPU	201 MB

Web

Device	Backend	Text latency
MacBook Pro M5	GPU	21.8 ms

IoT

Device	Backend	Text latency
Raspberry Pi 5 16GB	CPU	161 ms
Jetson Orin Nano	CPU	207 ms
Jetson Orin Nano	GPU	88 ms
Arduino VENTUNO Q	NPU	13.6 ms

Device	Backend	Text CPU memory
Raspberry Pi 5 16GB	CPU	282 MB
Jetson Orin Nano	CPU	302 MB
Jetson Orin Nano	GPU	523 MB
Arduino VENTUNO Q	NPU	206 MB