🤖Distributed LLM Runs on ESP32S3 Cluster
Tiny AI on Tiny Hardware
TL;DR
A new project runs a 0.5B parameter LLM across 7 ESP32S3 microcontrollers, showcasing the potential of distributed AI on low-power devices. This could be a game changer for edge computing.
A new project is running a 0.5B parameter language model on a cluster of 7 ESP32S3 microcontrollers. The architecture splits the model across nodes, with one ESP32S3 acting as the master node handling tokenization and embedding, while the others run the attention layer and MLP. This setup, using high-speed SPI Daisy-Chain communication, could be a game changer for edge computing, enabling complex AI tasks on low-power devices. The project uses 1.58-bit ternary quantization, reducing memory and computational requirements. This could be a big deal for anyone looking to run AI models on resource-constrained hardware.
Key Points
Distributed LLM runs on 7 ESP32S3 microcontrollers, each with 4MB of RAM and 448KB of flash memory.
Master node handles tokenization and embedding, while compute nodes run attention layer and MLP.
Nodes communicate through high-speed SPI Daisy-Chain, reducing latency and improving performance.
Project uses 1.58-bit ternary quantization, reducing memory and computational requirements.
Project is licensed under the MIT License and includes a workflow guide and README.
Why It Matters
If you're working on edge computing projects, this could be a big deal. Running a 0.5B parameter LLM on a cluster of ESP32S3 microcontrollers opens up possibilities for AI on resource-constrained hardware. This could be particularly useful for IoT devices, where power and memory constraints are significant.
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