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Free On-Device AI SDK to Run PyTorch on Mobile NPUs (Open Source)

Hacker News

Free On-Device AI SDK to Run PyTorch on Mobile NPUs (Open Source)

Hello HN, I’m Yeonseok, CEO of ZETIC.ai (ex-Qualcomm AI Research). Deploying AI models to mobile NPUs usually requires deep hardware knowledge and months of tuning. Today, we are changing that. We are opening our On-device AI SDK for free right now. We’ve released a GitHub repository that gives you direct access to full NPU acceleration on heterogeneous devices (Android/iOS). You don't need GPU Cloud infrastructure anymore. What you can do with this SDK (GitHub): Full NPU Speed: We unlock raw NPU power for heterogeneous devices globally. No more CPU lag. Bring Your Own Model: Don't just run our demos. You can upload your custom model and get an optimized, hardware-accelerated SDK in hours. Production-Ready Source Code: The repo includes full source code for On-device AI Agents, 40+ Language Translations, and SOTA Object Detection examples to get you started immediately. We want to democratize access to NPU computing. The GitHub repo is ready-to-use. I’d appreciate it if you could check the code and give it a Star if you find it useful. Repo: https://github.com/zetic-ai/ZETIC_MLange_apps Let me know if you have any questions about the NPU implementation!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, month · Missing: mobile apps, personal, entrepreneurs
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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