Running a 28.9M parameter LLM on an $8 microcontroller
Running a 28.9M parameter LLM on an $8 microcontroller Open to Work · 𝕏 slvDev · LinkedIn This is a 28.9 million parameter language model that generates text on an ESP32-S3, a microcontroller that costs about $8. It runs on the chip itself, with nothing sent to a server, and it writes each word to a small screen wired to the chip at roughly 9 tokens per second. The last language model people ran on a chip like this had 260 thousand parameters, so this one holds about a hundred times more.
- ▪Running a 28.9M parameter LLM on an $8 microcontroller Open to Work · 𝕏 slvDev · LinkedIn This is a 28.9 million parameter language model that generates text on an ESP32-S3, a microcontroller that costs about $8.
- ▪It runs on the chip itself, with nothing sent to a server, and it writes each word to a small screen wired to the chip at roughly 9 tokens per second.
- ▪The last language model people ran on a chip like this had 260 thousand parameters, so this one holds about a hundred times more.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/slvDev/esp32-ai |
| Publication time | Sat, 25 Jul 2026 18:59:50 +0000 |
| Retrieval time | 2026-07-25T19:07:22.626Z |
| Last seen | 2026-07-25T19:07:22.626Z |
| 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 | _gGGgyp0BTYB |
| 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
Running a 28.9M parameter LLM on an $8 microcontroller Open to Work · 𝕏 slvDev · LinkedIn This is a 28.9 million parameter language model that generates text on an ESP32-S3, a microcontroller that costs about $8. It runs on the chip itself, with nothing sent to a server, and it writes each word to a small screen wired to the chip at roughly 9 tokens per second. The last language model people ran on a chip like this had 260 thousand parameters, so this one holds about a hundred times more. It fits because most of the model lives in flash instead of RAM, using an idea from Google's Gemma models called Per-Layer Embeddings.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.