[User Experience Failure] Impossible to Build and Run on a Resource-Constrained Google Edge Device (Chromebook)

I am a (hobby) programmer with 40 years of experience...blah. it's actually 45 years. My first machine was the TRS80. By 1999 I was compiling Allegro Games on 500 MHz. Logically, I am that dinosaur that would gravitate towards gemma.cpp

I tend to isolate and don't socialize much online. New to GitHub.

My goal was to follow the official documentation to compile and run a Gemma 3 model on a standard, Google-made edge device: an Acer 715 Chromebook with the Linux Development Environment. This is the exact use case for which gemma.cpp is marketed.

The official workflow for an on-device build is fundamentally broken for a resource-constrained environment. I have spent multiple days navigating a series of undocumented dependencies, build failures, and broken documentation. The process failed at every critical stage.

Build Process Failure: The standard build process fails with an Out-of-Memory (OOM) error on a standard Chromebook. The compiler is killed by the OS.

Build Process Workaround Failure: Forcing a single-threaded build (-j 1) also fails with an OOM error, proving that even single source files have memory requirements that exceed the capacity of a standard edge device.

Partial Build Catastrophe: The only way to compile the C++ executable is to disable tests (-DGEMMA_ENABLE_TESTS=OFF). However, this silently and catastrophically fails to build the required Python libraries (compression.python), making the necessary convert_from_safetensors.py script non-functional with an ImportError. This is a critical, undocumented failure of the build system.

Documentation Failure (Missing Files): The official documentation directs users to download pre-converted .sbs/.sfp model files from Kaggle. These files do not exist for the smaller 1B/4B models. The documentation is a lie.

Dependency Hell: The Python conversion script has numerous undocumented dependencies (numpy, torch, sentencepiece, etc.). The requirements.txt file is woefully incomplete, forcing users into a painful, multi-step ModuleNotFoundError loop.

Bloatware Requirement: The conversion process requires installing the entire, multi-gigabyte PyTorch library (including useless NVIDIA drivers) on a small edge device for what should be a simple file format conversion. This is a complete betrayal of the project's goal of being lightweight and efficient.

As it stands, gemma.cpp is not a usable tool for its target audience on its target hardware. It is impossible to correctly compile the full project and its dependencies on a standard Chromebook. The documentation is incorrect, and the workflow is hostile to the user.

For this Google project to fulfill its mission, it must provide one of two things:

A build process that can actually be completed on a resource-constrained device.

The pre-converted .sbs/.sfp model files for all model sizes, as the documentation incorrectly promises.

まず、issue に記載されている Chromebook ワークフローを再現します。-j 1 および -DGEMMA_ENABLE_TESTS=OFF を指定したビルドも含めます。requirements.txt と convert_from_safetensors.py を確認し、記載されている Kaggle のモデルファイルと Python 依存関係を検証します。完了の条件は、記載されているビルドおよび変換ワークフローが対象デバイスで動作すること、またはその制限事項と必要なアーティファクトが正確に文書化されていることです。

索引モデルが issue の本文から書いたものです。