Quantized Gemma Model Inference on STM32MP257F-DK Board

[ramkumarkoppu]

Hi,

Could you share documentation or examples for running quantized foundational models (e.g. Google Gemma) on the STM32MP257F-DK—first in Python, then in C/C++ using the STM32MP2 NPU? Specifically:

• Does the STM32MP2 NPU support transformer-based architectures, or is it limited to CNNs (like the STM32N6)?

• Which inference frameworks are supported for GenAI on this platform? does this NPU ported by ST for llama.cpp ?

Sorry, couldn't find required info from STM32 MPU wiki pages.

Thanks!

 

 

[JS_PWS]

Hello

We are currently evaluating hardware options and have the same question. Can somebody from ST answer it here?

 

Thank you and best regards
Jan

[Steven-LIN]

Hello,

I have the same question. Is it possible to run LLMs on the STM32MP2 series?

Additionally, what is the expected performance/inference efficiency?

Thanks!

[VABRI]

Hello, 

The NPU architecture of the STM32MP2 series does not support transformer based architecture models.
LLM models can be run using the CPU.

The framework supported by the X-LINUX-AI package are listed in this wiki page:
https://wiki.st.com/stm32mpu/wiki/Category:X-LINUX-AI_expansion_package

BR

 

 

[PatrickF]

An example of local running LLM on STM32MP257F-EV1 (as said, using CPU only).
https://www.linkedin.com/posts/danilopietropau_another-great-example-of-llm-on-stm32mp2-activity-7293222309333495809-Qe0d

Regards.