Edge AI
Edge AI on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on edge ai in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around edge ai, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
FreeToken Unlocks Frontier MoE Inference on Consumer Hardware via Dynamic Co-Execution
FreeToken, a new open-source inference engine developed by researchers at UC Berkeley and MIT, significantly expands the accessibility of Mixture-of-Experts (MoE) models. This innovative system enables faster, more efficient AI inference directly on consumer hardware through dynamic co-execution. FreeToken’s optimized scheduling and weight management unlock powerful edge AI applications and pave the way for self-hosted reasoning systems. For those seeking a deeper understanding of optimizing LLMs, explore our related article, "Quantization and Pruning Methods to Make Your LLM Leaner.”
How to Leverage Local Small Language Models for Your Projects
Unlock AI power without relying on cloud services. This practical guide explores leveraging local Small Language Models (SLMs) – compact, privacy-preserving models you can run directly on your hardware. Experience faster processing, reduced costs, and enhanced control over your AI applications. Discover how to integrate these innovative tools into your projects for a future-focused approach to data management. For a deeper dive into AI governance considerations, explore our related article, "Microsoft Moves AI Governance From Policy to Runtime Enforcement."
No cloud, no GPUs, no problem: Liquid AI's new model LFM2.5-2.6B brings powerful AI agents to devices as small as a Raspberry Pi
Liquid AI has unveiled LFM2.5-2.6B, a new open-weight language model designed to bring powerful AI agents to devices as small as a Raspberry Pi – a significant step toward accessible edge AI. This model, boasting 2.6 billion parameters and a 128,000-token context window, runs entirely on local hardware without cloud inference or GPUs, ideal for high-volume tasks like automation and connectivity-limited environments. Explore how this innovative solution transforms data management and expands possibilities for enterprises, as highlighted in our recent coverage of Qwen 3.8-Max.
![I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]](https://preview.redd.it/wiyelkfpsifh1.jpeg?width=640&crop=smart&auto=webp&s=9ed353f6d1eab4c20efcaa110c0c5f642a6d6e99)
I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]
This impressive Bachelor's Final Project delivers a complete, from-scratch YOLO26n inference engine built using ARM64 Assembly Language and C—no frameworks required. The implementation prioritizes edge AI execution on Raspberry Pi 4, incorporating critical optimizations like ARM NEON SIMD, Winograd convolution, and cache-aware tiling. While performance gains didn't fully meet initial expectations, this project offers valuable insights into low-level neural network acceleration. For further exploration of efficient data encoding, consider “Ink & Switch Introduces Bijou64.” Repository: https://github.com/mohammad-ghaderi/YOLO26