Back to directory

Open Weight Models

A curated, actively maintained list of open-weight AI models selected for commercially exploitable licenses, no geographic restrictions, and verified benchmarks, organized by category and VRAM tier.

Open Weight Models

Open Weight Models is a curated list of open-weight AI models chosen against explicit selection criteria: a commercially exploitable license with no geographic restriction, and a VRAM footprint of 128 GB or less when quantized to Q4 for its main tier, with an extended tier for larger models. It was built to decide which models to support in a separate local LLM inference engine written in Rust and hand-written assembly.

Entries are grouped by category, generalists, code models, compact and edge models, reasoning and math models, vision and multimodal models, alternative architectures and web world models, and the list flags models whose license carries a revenue or monthly-active-user cap, warning that these are not interchangeable with a permissive license such as Apache 2.0 or MIT despite being listed for completeness.

It is updated periodically with dated addition notes, most recently in mid-2026.

More in Models

See all 405
  • mini-AGI

    github.com

    A continual learning language model that trains from scratch on a single 8 GB VRAM GPU, assembling its own architecture and adapting its size to the hardware and data available rather than shipping as a fixed pretrained model.

  • An open-source contrastive-embedding pipeline that maps technical CVE vulnerability descriptions to formal cybersecurity compliance controls in NIST SP 800-53 and CMMC, published with its model generation, fine-tuning and evaluation code.

  • peekaboolean

    github.com

    A small vision-language model, fine-tuned with LoRA on a 500-million-parameter backbone, that answers typed multiple-choice, ordinal-score and yes/no questions about an image with calibrated probabilities, in about 400 milliseconds on a laptop.