Real-Time Edge AI with the MemryX MX3 M.2 AI Accelerator Module скачать в хорошем качестве

Real-Time Edge AI with the MemryX MX3 M.2 AI Accelerator Module

In this Product of the Week, Robin Mitchell reviews the MemryX MX3 M.2 AI Accelerator Module - a compact M.2 PCIe Gen 3 card that transforms your system into a real-time edge AI powerhouse. Discover how its 80 million 4-bit-parameter chips deliver up to 6 TFLOPS each (and up to 96 TFLOPS in pipelined setups), all while drawing just 0.6–2 W per chip. Learn about: 💡 Key Features: PCIe Gen 3 M.2 2280 M-key form factor Up to 4 chips per module (current) supporting 4-bit & 8-bit weights 6 TFLOPS @ 1 GHz per chip (96 TFLOPS pipelined) 0.6–2 W power consumption per chip Framework support: ONNX, PyTorch, TensorFlow, Keras, TF Lite OS support: Ubuntu, Windows, Android Processor-agnostic: x86, ARM, RISC-V Environmental specs: –40 °C to 85 °C, 10–80 % RH 🏆 Win an MX3 M2! Leave a comment below telling us what you’d build with this module for your chance to win. Best comment takes it home! 👉 Get Yours: https://memryx.com/products 👉 GitHub Examples: https://github.com/memryx 📌 Chapters: 0:00 – Intro & Giveaway 0:33 – Introducing the MX3 M2 AI Accelerator 1:10 – Punch-to-Intelligence Ratio Explained 1:59 – Variants & Raw Performance Specs 2:23 – Power Efficiency & Weight Formats 2:47 – Framework & OS Compatibility 3:15 – Platform Agnosticism & Environmental Tolerance 3:49 – Real-Time Edge AI Applications 4:48 – Industrial & Desktop Use Cases 5:36 – GitHub Code Examples & Wrap-Up ❤️ Support Us: For all the latest products, projects and articles, visit our website at https://www.electromaker.io 💡 Stay Connected with Electromaker!    / @electromakerio     / electromaker.io     / electromakerio     / electromaker   👉 Get Yours: https://memryx.com/products 👉 GitHub Examples: https://github.com/memryx