Gemma Family
Collection
LiteRT models in the Gemma Family • 22 items • Updated • 102
litert-community/gemma-4-26B-A4B-it-litert-lm
Main Model Card: google/gemma-4-26B-A4B-it
This model card provides the Gemma 4 26B (A4B) mixture-of-experts model in LiteRT-LM format that is ready for deployment on web. Please check back here regularly for updates on wider platform support and further functionality improvements. The current LiteRT-LM version supports text; audio, image, and multitoken prediction support will be available in a future update.
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. This particular Gemma 4 model is a medium size so it is ideal for desktop use cases. By running this model on device, users can have private access to Generative AI technology without even requiring an internet connection.
These models are provided in the .litertlm format for use with the LiteRT-LM framework. LiteRT-LM is a specialized orchestration layer built directly on top of LiteRT, Google’s high-performance multi-platform runtime trusted by millions of Android and edge developers. LiteRT provides the foundational hardware acceleration via XNNPack for CPU and ML Drift for GPU. LiteRT-LM adds the specialized GenAI libraries and APIs, such as KV-cache management, prompt templating, and function calling. This integrated stack is the same technology powering the Google AI Edge Gallery showcase app.
Try Gemma 4 26B (A4B)
Build with Gemma 4 26B (A4B) and LiteRT-LM
Ready to integrate this into your product? Get started with LiteRT-LM documentation.
Gemma 4 26B (A4B) Performance on LiteRT-LM
Benchmarks were taken in Chrome using 1024 prefill tokens and 256 decode tokens with a context length of 1280 tokens. The model can support up to 195k context length.
Web
| Device | Backend | Quantization | Prefill (tokens/sec) | Decode (tokens/sec) | Time-to-first-token (sec) | Model size (MB) | GPU Memory (MB) | Peak CPU Memory (MB) |
|---|---|---|---|---|---|---|---|---|
| MacBook Pro M4 (M4 Max) | GPU | Q4_0 | 968 | 51 | 14.0 | 15787 | ~15000 | ~3600 |
- Web on LiteRT-LM uses a specially optimized model for Web because of its unique memory constraints. Currently the model is text-only.
- Q4_0: QAT quantized model with blockwise int4 weights and float activations.
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