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.
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
Wow, NexaSDK for Mobile looks incredible! The on-device AI with no cloud costs is a total game changer. Super curious, how does it handle model updates & maintenance in offline environments?
Great project. Can you process video streams, or do you just break them into frames, turn the video into photos, and then recognize them?
Hi, so this is basically a tool that has libraries that make AI integrations easier? Could u pls tell me what this does in 1 sentence?
Hi, I'm curious—what kinds of AI models does NexaSDK actually support out of the box? Are we talking language models, vision, speech, or all of them? And when it comes to running these models on device, is there a minimum RAM requirement you recommend for smooth performance? Would love to know before diving in—thanks!
Any plans for on-device guardrails / safety filters? (Especially for multimodal inputs)
⭐️ Impressive mobile AI SDK!
NexaSDK for Mobile makes it incredibly easy to bring powerful AI features to iOS and Android with just a few lines of code. The fact that everything runs fully on-device is a huge win — better privacy, no cloud dependency, and lower costs. Performance is outstanding, with clear optimizations for Apple Neural Engine and Snapdragon NPUs, delivering faster inference and excellent energy efficiency. Support for LLMs, vision, audio, and multimodal use cases makes this SDK very flexible. A great choice for developers building serious mobile AI apps. 🚀
This looks amazing! So you're making models work seamlessly across different devices through repackaging? I'd love to learn more about what NEXA_TOKEN does, how the whole system works under the hood, and which models I can use!
Can the AI surface alternative interpretations or counterarguments to the author’s views?
How would you describe Readever to someone who’s only used summary-based reading tools before?
very cool idea. Can we also guarantee the data/operation access for it in future, so the model may operate on multiple mobile apps for me?
Congrats on the launch! On-device AI that respects user privacy without killing performance is something mobile teams really need.
Can I do on-device RAG easily? Like embeddings + local vector store + rerank + LLM?
This is huge for cutting cloud costs. We've been hesitant to add heavy multimodal features just because of the API bills and latency, but running this on-device solves both. 9x energy efficiency is wild if it holds up in real-world usage. Does this support custom quantized models yet?
How does NexaSDK help developers reduce cost and privacy risk compared to cloud AI solutions?
Love seeing Snapdragon NPU + Apple Neural Engine mentioned explicitly. Most SDKs hand-wave performance.
Can I do on-device RAG easily? Like embeddings + local vector store + rerank + LLM?
How does NexaSDK leverage mobile NPUs like Apple Neural Engine and Snapdragon NPU for acceleration?
How do you handle device fragmentation on Android? (Different NPUs, drivers, OS versions, etc.)
Is this the client-side rendering version? Do you have any performance or quality evaluation results? How much storage space does it take up?
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?