This model card provides a few variants of the EmbeddingGemma model that are ready for deployment on Android and iOS using
LiteRT
, or on Android via the
Google AI Edge RAG Library
.
Use the sentencepiece model as the tokenizer for the EmbeddingGemma model.
Performance
Android
Note that all benchmark stats are from a Samsung S25 Ultra.
Backend
Quantization
Max sequence length
Init time (ms)
Inference time (ms)
Memory (RSS in MB)
Model size (MB)
GPU
Mixed Precision*
256
1175
64
762
179
GPU
Mixed Precision*
512
1445
119
762
179
GPU
Mixed Precision*
1024
1545
241
771
183
GPU
Mixed Precision*
2048
1707
683
786
196
CPU
Mixed Precision*
256
17.6
66
110
179
CPU
Mixed Precision*
512
24.9
169
123
179
CPU
Mixed Precision*
1024
35.4
549
169
183
CPU
Mixed Precision*
2048
35.8
2455
333
196
*Mixed Precision refers to per-channel quantization with int4 for embeddings, feedforward, and projection layers, and int8 for attention (e4_a8_f4_p4).
Notes:
Init time: the cost paid once per application initialization – subsequent inferences do not pay this cost
Memory: indicator of peak RAM usage
Model Size: measured by the size of the .tflite flatbuffer (serialization format for LiteRT models)
The inference on CPU is accelerated via the LiteRT
XNNPACK
delegate with 4 threads
Benchmark is run with cache enabled and initialized. During the first run, the latency may differ.
Runs of litert-community embeddinggemma-300m on huggingface.co
1.2K
Total runs
28
24-hour runs
-188
3-day runs
-238
7-day runs
-1.0K
30-day runs
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