ESP32-AI: Running a 28.9M-Parameter LLM on an $8 Microcontroller
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ESP32-AI is a lightweight local AI project that runs a 28.9M-parameter TinyStories language model on an $8 ESP32-S3 microcontroller, generating text offline with no cloud dependency and about 9.5 tokens per second. Key Takeaways A 28.9M-parameter TinyStories LLM runs fully offline on an ESP32-S3 using Per-Layer Embeddings. Per-layer embedding tables live in flash via execute-in-place memory mapping, leaving dense core weights at about 560KB in SRAM. Performance lands around 9.5 tokens per…
1Key Takeaways
- ESP32-AI is a lightweight local AI project that runs a 28.9M-parameter TinyStories language model on an $8 ESP32-S3 microcontroller, generating text offline with no cloud dependency and about 9.5 tokens per second.
- Key Takeaways A 28.9M-parameter TinyStories LLM runs fully offline on an ESP32-S3 using Per-Layer Embeddings.
- Per-layer embedding tables live in flash via execute-in-place memory mapping, leaving dense core weights at about 560KB in SRAM.
- Performance lands around 9.5 tokens per….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that eSP32-AI is a lightweight local AI project that runs a 28.9M-parameter TinyStories language model on an $8 ESP32-S3 microcontroller, generating text offline with no cloud dependency and about 9.5 tokens per second.
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