GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
A minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL.
Good fit when
- When you want a pure Rust implementation that provides an easy-to-understand basis for learning about the inner workings of AI models.
- For scenarios where the use of lightweight OpenCL on both NVIDIA and AMD GPUs is preferred over CUDA-specific setups.
Avoid when
- When high performance is required as femtoGPT operates relatively slower compared to optimized models, especially for large-scale training.
- If your project strictly needs CUDA-based optimization specific to NVIDIA GPUs, given that femtoGPT leverages OpenCL for GPU support.
- Requirements:
- Requires the Rust toolchain installed on your system.; If targeting GPU usage, correct installation of GPU drivers along with OpenCL runtimes is necessary.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (321d since push)
- As of Sep 7, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 7, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
cargo add femtoGPTSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
femtoGPT is an open-source project offering a pure Rust implementation for training and inference on GPT-style language models, supporting both CPU and GPU via OpenCL.
Capability facts
- Languages
- rust
Source: github.language · Sep 7, 2026
Categories
Tags
README
:robot: femtoGPT femtoGPT is a pure Rust implementation of a minimal Generative Pretrained Transformer. It can be used for both inference and training of GPT style language models using CPUs and GPUs ! ( HEY! I'm also writing a book, which will soon discuss the implementation of...
For agents
This page has a .md twin and JSON over the API.