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gemma-4-12B-it-litert-lm
cKernel/gemma-4-12B-it-litert-lm
gemma-4-12B-it-litert-lm is a machine learning model from cKernel. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for litert-lm. The card lists the license as apache-2.0.
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At a glance
- Library
- litert-lm
- License
- apache-2.0
- Access
- Public
- Created
- Jul 27, 2026
- Updated
- Jul 27, 2026
- SHA
- a6c1c5ea
Base models
Model card
From the Hugging Face model README
litert-community/gemma-4-12B-it-litert-lm
Main Model Card: google/gemma-4-12B-it
This model card provides the Gemma 4 12B model in LiteRT-LM format that is ready for deployment on macOS and linux, as well as web in a more limited capacity. Please check back here regularly for updates on wider platform support and further functionality improvements. The current LiteRT-LM version supports text and audio modalities, image and multitoken prediction support will be avaialble 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 12B
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Build with Gemma 4 12B and LiteRT-LM
Ready to integrate this into your product? Get started with LiteRT-LM documentation.
Gemma 4 12B Performance on LiteRT-LM
All benchmarks were taken using 1024 prefill tokens and 256 decode tokens with a context length of 2048 tokens via LiteRT-LM. The model can support up to 32k context length. Time-to-first-token does not include load time. Benchmarks were run with caches enabled and initialized. During the first run, the latency and memory usage may differ. Model size is the size of the file on disk.
The Linux GPU memory usage number was measured with --disable_cache=true --convert_weights_on_gpu=false. Otherwise it will be about 6GB higher.
Linux
| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | GPU Memory (MB) |
|---|---|---|---|---|---|---|
| AMD Radeon™ AI PRO R9700 | GPU | 662.32 | 66.26 | 1.56 | 6235 | 8064.2 |
macOS
| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | GPU Memory (MB) |
|---|---|---|---|---|---|---|
| MacBook Pro M4 | GPU | 243.55 | 29.56 | 4.2 | 6235 | 7763 |
Web
| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | GPU Memory (MB) | Peak CPU Memory (MB) |
|---|---|---|---|---|---|---|---|
| MacBook Pro M4 (M4 Max) | GPU | 388 | 26 | 3.5 | 5986 | ~7700 | ~1200 |
- Web on LiteRT-LM uses a specially optimized model for Web because of its unique memory constraints. Currently the model is text-only.
- Benchmarks taken in Chrome using a context length of 1280. The model can support up to 195k context length.
- Library
- litert-lm
- License
- apache-2.0
- Created
- Jul 27, 2026
- Updated
- Jul 27, 2026