GitHub – slvDev/esp32-ai · GitHub
This is a 28.9 million parameter language model that generates text on an ESP32-S3 microcontroller. It runs on the chip itself, with nothing sent to a server, and it displays generated text at 9.88 tokens per second on a small screen wired to the chip. It fits because most of the model lives in flash instead of RAM, using Per-Layer Embeddings, an idea from Google’s Gemma 3n.
Thanks to Michael Stauffer
RecursiveMAS, a recursive multi-agent framework that casts the
entire system as a unified latent-space recursive computation
A Comprehensive Survey of Small Language Models: Technology, On-Device Applications, Efficiency, Enhancements for LLMs, and Trustworthiness
GitHub – FairyFali/SLMs-Survey: Survey of Small Language Models from Penn State, … · GitHub
Source Michael Stauffer
LFM2.5-230M https://www.liquid.ai/blog/lfm2-5-230m on a Unitree G1 https://www.youtube.com/shorts/CuMOWa2y1Ho, running on NVIDIA Jetson Orin. it takes a single natural-language instruction and decomposes it into a sequence of tool calls that invoke pre-trained low-level skills provided by NVIDIA’s SONIC framework
Thanks to Michael Stauffer for providing it