Unlocking On-Device AI with Gemini Nano and the Future of Private Intelligence

We recently announced NimbleEdge AI - a privacy-first, always-on on-device AI assistant that operates entirely on smartphones. With zero reliance on the cloud and ultra-low latency, it delivers fast, secure and contextually aware intelligence powered by the NimbleEdge on-device AI platform. By ensuring that user data never leaves the device and optimizing models for real-time inference, we are reimagining the mobile AI experience.

Today, we are pleased to share a significant advancement: seamless integration with Google’s Gemini Nano, ushering in a new era of mobile large language model (LLM) deployment.

A New Era: Gemini Nano + NimbleEdge for Android Ecosystem

Thanks to newly available Gemini Nano capabilities on select Android devices, the NimbleEdge AI Assistant can now leverage system-integrated LLMs directly via the operating system. This eliminates the need for model downloads or increased application size, enabling truly efficient, scalable, and high-performance on-device intelligence.

Gemini Nano is a system-level on-device LLM, accessible through Google’s AICore system service. It manages model updates, safety filtering, and inference acceleration via native hardware. Through our integration, developers using the NimbleEdge platform can:

• Invoke Gemini Nano via Python-based workflows
• Stream LLM outputs in real time
• Combine Gemini Nano with custom on-device AI models e.g. NimbleEdge AI app uses Llama as a fallback on devices where Gemini Nano is not available
• Build cloud-free, conversational AI applications

This capability is currently supported on Pixel 9 series devices and will expand with future hardware iterations.

No Downloads. No Trade-offs in Android LLM Deployment

Historically, deploying on-device LLMs required bundling models with the application or downloading them upon initial launch. This increased app size, degraded user experience, and added architectural complexity.

With Gemini Nano, these trade-offs are eliminated. Developers now benefit from reduced storage overhead, zero user friction, and a significant boost in efficiency. This represents a paradigm shift for building private AI applications, enabling real-time natural language processing and on-device generative AI capabilities.

How to Access Gemini Nano

To begin integrating Gemini Nano with NimbleEdge:

1. Currently, this is only available as experimental access on Pixel 9 series phones. Make sure you have one handy.
2. Join the aicore-experimental Google group.
3. Opt into the Android AICore testing program.
4. The app name for “Android AICore” on Play store should change to “Android AICore (Beta)”. Update to the latest version.
5. Open Private Compute Service APK on Play store and update the app. Ensure app version is 1.0.release.658389993 or higher.
6. Restart your phone and wait for a few minutes. Ensure AICore app version starts with 0.thirdpartyeap.

After setup, developers can create Python Workflow Scripts within the NimbleEdge SDK to initiate Gemini Nano LLM inference and stream results to their application interface.

Sample Python Script for interfacing with Gemini Nano:

Using Gemini Nano in the NimbleEdge platform is super easy through our on-device Python Workflow Scripts which allows you to write LLM execution and post-processing logic directly in Python. Read more about them here.

With minimal code, applications can deliver robust on-device language model performance, entirely independent of the cloud.

The Strategic Importance of On-Device AI and LLMs

The integration of Gemini Nano represents a key milestone in the advancement of on-device AI, mobile LLM applications, and privacy-preserving infrastructure. Industry leaders are converging on this approach:

• Apple Intelligence enables LLMs on iOS with a privacy-first framework along with their recent announcement of ‘Foundation Model Framework’.
• Samsung accelerates its NPU-driven edge AI roadmap.
• Meta, Qualcomm, and Nvidia are advancing on-device model inference.

The shift is unmistakable: AI is moving from the cloud to the device.

This change is more than a technical optimization—it redefines the user-AI relationship:

• Inference happens directly on end-user devices.
• Sensitive data remains local.
• Personalization occurs dynamically and securely.
• Trust is reinforced through transparent, offline processing.

NimbleEdge’s Vision for On-Device AI Innovation

At NimbleEdge, we view this industry transition as fundamental to the next generation of AI - private, adaptive, and always-available intelligence. We are developing a comprehensive, developer-centric ecosystem for high-performance AI workflows executed entirely on-device.

Our platform enables:

• Full-stack on-device AI pipelines: including LLMs, speech recognition, text-to-speech, embeddings, and classifiers
• Seamless fusion of Gemini Nano and proprietary Android-compatible LLMs
• Cloud-free implementation of voice assistants and multi-modal generative AI applications
• Up to 90% reduction in cloud inference costs via local model execution

What’s Next for NimbleEdge

In the coming months, we will roll out:

• An enhanced NimbleEdge SDK with native support for agentic workflows
• Multi-model orchestration to support hybrid LLM routing (on-device and remote)
• Hardware-optimized models via Sparse Transformers and quantization techniques
• On-device infrastructure to support continual, cross-device model personalization

Our mission is to empower developers to build powerful, efficient, and trustworthy AI applications by reimagining intelligence on-device.

Building the Future of AI - Private, Local and Personal

The era of monolithic, cloud-dependent AI is coming to a close. The next generation of intelligent systems will be decentralized, efficient, and embedded seamlessly into everyday devices.

As Gemini Nano and NimbleEdge converge, we are ushering in a new standard for AI: always available, always local, and always private.

If you are building mobile experiences or AI-enabled apps, we welcome you to explore what’s possible with NimbleEdge.

Try It Today

Register now to access NimbleEdge AI with Gemini Nano support.

We would love to hear from you - email us your thoughts at team-ai@nimbleedgehq.ai or join our Discord.