NexaSDK for Mobile
Easiest solution to deploy multimodal AI to mobile
637 followers
Easiest solution to deploy multimodal AI to mobile
637 followers
NexaSDK for Mobile lets developers use the latest multimodal AI models fully on-device on iOS & Android apps with Apple Neural Engine and Snapdragon NPU acceleration. In just 3 lines of code, build chat, multimodal, search, and audio features with no cloud cost, complete privacy, 2x faster speed and 9× better energy efficiency.
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Hey Product Hunt — I’m Zack Li, CTO and co-founder of Nexa AI 👋
We built NexaSDK for Mobile after watching too many mobile app development teams hit the same wall: the best AI experiences want to use your users’ real context (notes, photos, docs, in-app data)… but pushing that to the cloud is slow, expensive, and uncomfortable from a privacy standpoint. Going fully on-device is the obvious answer — until you try to ship it across iOS + Android with modern multimodal models.
NexaSDK for Mobile is our “make on-device AI shippable” kit. It lets you run state-of-the-art models locally across text + vision + audio with a single SDK, and it’s designed to use the phone’s NPU (the dedicated AI engine) so you get ~2× faster inference and ~9× better energy efficiency — which matters because battery life is important.
What you can build quickly:
On-device LLM copilots over user data (messages/notes/files) — private by default
Multimodal understanding (what’s on screen / in camera frames) fully offline
Speech recognition for low-latency transcription & voice commands
Plus: no cloud API cost, day-0 model support, and one SDK across iOS/Android
Try today at: https://sdk.nexa.ai/mobile, I’d love your real feedback:
What’s the first on-device feature you’d ship if it was easy?
What’s your biggest blocker today — model support, UX patterns, or performance/battery?
@zack_learner We look forward to hearing everyone's feedback! Feel free to ask us any questions.
@zack_learner How does NexaSDK handle different NPUs across devices? Is performance consistent on older phones too?
@zack_learner @masump Great question. NexaSDK uses our NexaML runtime as an abstraction layer: at runtime we detect the device’s available accelerators (Apple Neural Engine / Snapdragon NPU / GPU / CPU) and route each model/operator to the best backend for that device. Same app code — the SDK handles the hardware differences.
On older phones, performance won’t be identical (hardware is the bottleneck), but it’s predictable: we automatically fall back to GPU/CPU when an NPU isn’t available
@masump We support Android with Snapdragon Gen4 NPU (SAMSUNG S25) IOS & MacOS (iPhone 12+). The performance is consistent from all compatible devices. For those devices without NPU support, you can also use the GPU & CPU version of SDK.
@zack_learner Congrats! Very anticipated. Been waiting for such SDK with dynamic LLM loading. Please add a text/image generation examples in Docs for Qwen in example. Will be very popular.
@anton_gubarenko Yes, we have supported Qwen model, see: https://docs.nexa.ai/nexa-sdk-android/overview#supported-models
Qwen3-4B is supoprted for PC/mobile, and Qwen3VL is supported for PC
Very impressive! So, you re-package models to make them compatible with different devices? What is `NEXA_TOKEN` needed for? Maybe you could quickly explain how does it work and which models are available?
@avloss We have our internal convert pipeline and quantization algorithm make model compatible for difference devices. For NPU inference usages on PC, `NEXA_TOKEN` is needed for 1st time to validate the device, since NPU inference is only free for individual developers.
Local AI modals are definitely the future! Wondering how do you price this product. Is it free? Because pricing stuff running locally is quite tricky.
@nikitaeverywhere Yes, nexaSDK is free, we only charge for large enterprise adoption for NPU inference.
@nikitaeverywhere Yes this product is free for you to use! We believe local AI will be in every device in the future. Please feel free to let me know your feedback.
Really exciting work on bringing ondevice AI to mobile, love the focus on performance and privacy.
How smooth is the integration with existing iOS/Android apps? Any recommended examples or best practices to help developers get started quickly? I might actually use it in my new app :)
@nilni Thanks Nil for the support. With just 3 lines of code you get integrate it into your apps. Check out our quickstart:
Android: https://docs.nexa.ai/nexa-sdk-android/overview
iOS: https://docs.nexa.ai/nexa-sdk-ios/overview
I‘m gonna try this with my new iPhone :D
How does NexaSDK handle memory constraints, power efficiency, and thermal limits on mobile devices?
@cheng_ju1 Awesome! Please let us know your feedback. NexaSDK optimizes models so that they can fit in a mobile device with memory constraints. And also NexaSDK can run the models on NPU, which is 9X more energy efficient than other SDKs who are using CPU only for model inference.
On-device is clearly the right answer for anything touching real user context — notes, messages, photos, screen content. Curious to see how teams use this for:
screen-aware copilots
offline multimodal assistants
privacy-sensitive workflows
...
@llnx Exactly! On-device AI will be powering every app by 2030!
@llnx I cannot agree more, thanks
Congrats on the launch! Using models locally is always a better choice in terms of privacy, still i want to know more about the privacy and security you are providing.
@anishsharma Thanks Anish. Yes, local AI is the perfect choice for privacy. NexaSDK is 100% local and offline and none of your data will leave your device when running AI models. Please feel free to let me know any other feedback or questions.