HN: I ran a 56M parameter LLM across three ESP32-S3 boards via ESP-NOW
ESP32-S3 Distributed AI Distributed micro-LLM inference across three ESP32-S3 N16R8 boards with ESP-NOW communication. Overview This project implements a distributed AI system that runs a 56M-parameter language model across three ESP32-S3 microcontrollers. Inspired by slvDev/esp32-ai, which demonstrated running TinyStories on a single board, this project extends the architecture to a multi-board distributed system with web-based interaction.
- ▪ESP32-S3 Distributed AI Distributed micro-LLM inference across three ESP32-S3 N16R8 boards with ESP-NOW communication.
- ▪Overview This project implements a distributed AI system that runs a 56M-parameter language model across three ESP32-S3 microcontrollers.
- ▪Inspired by slvDev/esp32-ai, which demonstrated running TinyStories on a single board, this project extends the architecture to a multi-board distributed system with web-based interaction.
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/wladimiravila/esp32s3-distributed-ai |
| Publication time | Thu, 30 Jul 2026 15:07:32 +0000 |
| Retrieval time | 2026-07-30T15:12:02.101Z |
| Last seen | 2026-07-30T15:12:02.101Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 5DXYU-vYTSnr · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
ESP32-S3 Distributed AI Distributed micro-LLM inference across three ESP32-S3 N16R8 boards with ESP-NOW communication. Overview This project implements a distributed AI system that runs a 56M-parameter language model across three ESP32-S3 microcontrollers. Inspired by slvDev/esp32-ai, which demonstrated running TinyStories on a single board, this project extends the architecture to a multi-board distributed system with web-based interaction. The model is trained on WikiText-103 (Wikipedia corpus) using Per-Layer Embeddings (PLE) from Google's Gemma architecture, quantized to 4-bit, and split across three boards that communicate via ESP-NOW wireless protocol. The 50.3M-parameter PLE table is split across Board A and Board B (Split-PLE) to fit within the 16MB flash per board.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.