--- title: "local-llms-on-android vs recurrentgemma" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dineshsoudagar-local-llms-on-android-vs-google-deepmind-recurrentgemma" tools: ["dineshsoudagar-local-llms-on-android", "google-deepmind-recurrentgemma"] --- # local-llms-on-android vs recurrentgemma *GraphCanon updated Aug 13, 2026* ## Verdict Pick local-llms-on-android if local-llms-on-android enables offline deployment and execution of large language models on Android devices using LiteRT and ONNX Runtime for applications requiring real-time conversation; pick recurrentgemma if recurrentGemma is an open-source language model from Google DeepMind, utilizing the Griffin architecture and supporting JAX and PyTorch for efficient neural network training and inference on TPUs, CPUs, and GPUs. [local-llms-on-android](https://github.com/dineshsoudagar/local-llms-on-android) reports 381 GitHub stars, 45 forks, and 12 open issues, last pushed Aug 11, 2026. [recurrentgemma](https://github.com/google-deepmind/recurrentgemma) has 684 stars, 40 forks, and 4 open issues, last pushed Feb 6, 2026. Figures are from public GitHub metadata via [local-llms-on-android's repository](https://github.com/dineshsoudagar/local-llms-on-android) and [recurrentgemma's repository](https://github.com/google-deepmind/recurrentgemma). | | [local-llms-on-android](/tools/dineshsoudagar-local-llms-on-android.md) | [recurrentgemma](/tools/google-deepmind-recurrentgemma.md) | | --- | --- | --- | | Tagline | Run local LLMs for offline chat and question answering on Android. | Open weights language model from Google DeepMind, based on Griffin | | Stars | 381 | 684 | | Forks | 45 | 40 | | Open issues | 12 | 4 | | Language | Kotlin | Python | | Adopt for | local-llms-on-android enables offline deployment and execution of large language models on Android devices using LiteRT and ONNX Runtime for applications requiring real-time conversation. | RecurrentGemma is an open-source language model from Google DeepMind, utilizing the Griffin architecture and supporting JAX and PyTorch for efficient neural network training and inference on TPUs, CPUs, and GPUs. | | Persona | - | - | | Runtime | - | - | | License | MIT | The codebase is distributed under the permissive Apache License, version 2.0, allowing for broad usage but with no warranties expressed or implied. | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [local-llms-on-android](/tools/dineshsoudagar-local-llms-on-android.md) | [recurrentgemma](/tools/google-deepmind-recurrentgemma.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 181d | | Open issues (now) | 12 | 4 | | Owner type | User | Organization | | Full report | [trust report](/tools/dineshsoudagar-local-llms-on-android/trust.md) | [trust report](/tools/google-deepmind-recurrentgemma/trust.md) | ## Decision facts: local-llms-on-android - **Requirements:** Supports deployment on Android devices using Kotlin programming language with LiteRT engine and ONNX Runtime for inference. - **Adopt for:** local-llms-on-android enables offline deployment and execution of large language models on Android devices using LiteRT and ONNX Runtime for applications requiring real-time conversation. ## Decision facts: recurrentgemma - **Requirements:** Optimized for TPU using the Flax implementation.; Supports CPU and GPU environments via JAX and PyTorch. - **Adopt for:** RecurrentGemma is an open-source language model from Google DeepMind, utilizing the Griffin architecture and supporting JAX and PyTorch for efficient neural network training and inference on TPUs, CPUs, and GPUs. - **License detail:** The codebase is distributed under the permissive Apache License, version 2.0, allowing for broad usage but with no warranties expressed or implied. ## Choose when ### Choose local-llms-on-android if… - local-llms-on-android is primarily Kotlin; recurrentgemma is Python. - License: local-llms-on-android is MIT, recurrentgemma is Apache-2.0. - Requirements: Supports deployment on Android devices using Kotlin programming language with LiteRT engine and ONNX Runtime for inference.. - Tags unique to local-llms-on-android: android, chatbot, gemma4, huggingface-tokenizers. - To enable offline chat and question answering capabilities, particularly when Gemma, Qwen, or LLaMA are the preferred model architectures. ### Choose recurrentgemma if… - recurrentgemma is primarily Python; local-llms-on-android is Kotlin. - License: recurrentgemma is Apache-2.0, local-llms-on-android is MIT. - Requirements: Optimized for TPU using the Flax implementation.; Supports CPU and GPU environments via JAX and PyTorch.. - Tags unique to recurrentgemma: deep-learning, flax, jax, language-model. - If you require high efficiency in neural network training or inferencing specifically optimized for TPUs using the Flax implementation ## When NOT to use local-llms-on-android - If real-time connection with internet-based services is necessary for chatbot operation and interaction. - In scenarios where the model's computational requirements exceed the capabilities of the target Android device, possibly leading to performance issues or excessive battery drain. ## When NOT to use recurrentgemma - Do not use if your infrastructure does not support TensorFlow, since RecurrentGemma optimizes efficiency mostly on TPUs which are tightly coupled with TensorFlow's ecosystem - Avoid using this tool if you are working in a restricted environment where setting up virtual environments with tools like Poetry or manually managing dependencies with pip is challenging ## Common questions ### What is the difference between local-llms-on-android and recurrentgemma? local-llms-on-android: Run local LLMs for offline chat and question answering on Android.. recurrentgemma: Open weights language model from Google DeepMind, based on Griffin. See the comparison table for live GitHub stats and shared categories. ### When should I choose local-llms-on-android over recurrentgemma? Choose local-llms-on-android over recurrentgemma when local-llms-on-android is primarily Kotlin; recurrentgemma is Python; License: local-llms-on-android is MIT, recurrentgemma is Apache-2.0; Requirements: Supports deployment on Android devices using Kotlin programming language with LiteRT engine and ONNX Runtime for inference.; Tags unique to local-llms-on-android: android, chatbot, gemma4, huggingface-tokenizers; To enable offline chat and question answering capabilities, particularly when Gemma, Qwen, or LLaMA are the preferred model architectures. ### When should I choose recurrentgemma over local-llms-on-android? Choose recurrentgemma over local-llms-on-android when recurrentgemma is primarily Python; local-llms-on-android is Kotlin; License: recurrentgemma is Apache-2.0, local-llms-on-android is MIT; Requirements: Optimized for TPU using the Flax implementation.; Supports CPU and GPU environments via JAX and PyTorch.; Tags unique to recurrentgemma: deep-learning, flax, jax, language-model; If you require high efficiency in neural network training or inferencing specifically optimized for TPUs using the Flax implementation. ### When should I avoid local-llms-on-android? If real-time connection with internet-based services is necessary for chatbot operation and interaction. In scenarios where the model's computational requirements exceed the capabilities of the target Android device, possibly leading to performance issues or excessive battery drain. ### When should I avoid recurrentgemma? Do not use if your infrastructure does not support TensorFlow, since RecurrentGemma optimizes efficiency mostly on TPUs which are tightly coupled with TensorFlow's ecosystem Avoid using this tool if you are working in a restricted environment where setting up virtual environments with tools like Poetry or manually managing dependencies with pip is challenging ### Is local-llms-on-android or recurrentgemma more popular on GitHub? recurrentgemma has more GitHub stars (684 vs 381). Stars measure visibility, not whether either tool fits your constraints. ### Are local-llms-on-android and recurrentgemma open source? Yes - both are open-source projects on GitHub (local-llms-on-android: MIT, recurrentgemma: Apache-2.0). ### Where can I find alternatives to local-llms-on-android or recurrentgemma? GraphCanon lists graph-backed alternatives at [local-llms-on-android alternatives](/tools/dineshsoudagar-local-llms-on-android/alternatives) and [recurrentgemma alternatives](/tools/google-deepmind-recurrentgemma/alternatives) ([local-llms-on-android markdown twin](/tools/dineshsoudagar-local-llms-on-android/alternatives.md), [recurrentgemma markdown twin](/tools/google-deepmind-recurrentgemma/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dineshsoudagar-local-llms-on-android-vs-google-deepmind-recurrentgemma.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, local-llms-on-android or recurrentgemma? local-llms-on-android: Very active. recurrentgemma: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for local-llms-on-android and recurrentgemma? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [local-llms-on-android trust report](/tools/dineshsoudagar-local-llms-on-android/trust); [recurrentgemma trust report](/tools/google-deepmind-recurrentgemma/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dineshsoudagar-local-llms-on-android`](/api/graphcanon/graph?tool=dineshsoudagar-local-llms-on-android) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_