Hi Gemma folks,
I wanted to share a small but unusual language-runtime project that may still be relevant to the broader question of how far lightweight language capability can be pushed, even though it sits far outside the normal CPU/GPU/TPU dense-model path.
We built a public demo line called Engram and deployed it on a commodity ESP32-C3.
Current public numbers:
Important scope note:
This is not presented as unrestricted open-input native LLM generation on MCU.
The board-side path is closer to a flash-resident, table-driven runtime with:
- packed token weights
- hashed lookup structures
- fixed compiled probe batches
- streaming fold / checksum style execution over precompiled structures
So this is not a standard lightweight dense model running on a smaller device. It is closer to a task-specialized language runtime whose behavior has been crystallized into a compact executable form under severe physical constraints.
Repo:
https://github.com/Alpha-Guardian/Engram
Why I’m posting here is that Gemma seems to represent one of the clearest public efforts around lightweight open language models with practical inference paths.
What I’d be curious about is whether systems like this should be thought of as:
- completely outside the lightweight language-model family
- an extreme endpoint where some task capability is no longer best served by even a compact dense model
- or an early sign that future language systems may combine lightweight dense models with highly specialized executable forms for certain capability slices
If this direction is relevant to your team, I’d be glad to compare notes.
Hi Gemma folks,
I wanted to share a small but unusual language-runtime project that may still be relevant to the broader question of how far lightweight language capability can be pushed, even though it sits far outside the normal CPU/GPU/TPU dense-model path.
We built a public demo line called Engram and deployed it on a commodity ESP32-C3.
Current public numbers:
Host-side benchmark capability
LogiQA = 0.392523IFEval = 0.780037Published board proof
LogiQA 642 = 249 / 642 = 0.3878504672897196host_full_match = 642 / 6421,380,771 bytesImportant scope note:
This is not presented as unrestricted open-input native LLM generation on MCU.
The board-side path is closer to a flash-resident, table-driven runtime with:
So this is not a standard lightweight dense model running on a smaller device. It is closer to a task-specialized language runtime whose behavior has been crystallized into a compact executable form under severe physical constraints.
Repo:
https://github.com/Alpha-Guardian/Engram
Why I’m posting here is that Gemma seems to represent one of the clearest public efforts around lightweight open language models with practical inference paths.
What I’d be curious about is whether systems like this should be thought of as:
If this direction is relevant to your team, I’d be glad to compare notes.