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- WBWe built a knowledge hub for running LLMs on edge devices2024 · github.com · ▲13
Hey HN! Alex and Zack from Nexa AI here. We are excited to share a project our team has been passionately working on recently, in collaboration with Jiajun from Meta, Qun from San Francisco State University, and Xin and Qi from the University of North Texas. Running AI models on edge devices is becoming increasingly important. It's cost-effective, ensures privacy, offers low-latency responses, and allows for customization. Plus, it's always available, even offline. What's really exciting is that smaller-scale models are now approaching the performance of large-scale closed-source models for…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 18d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 19d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 28d ago · cactuscompute.com
