ggml vs Hugging Face

ggml (AI inference at the edge) and Hugging Face (The AI community building the future.) both appear on the Smoower Developer Ecosystem Index.

Hugging Face (rank #2) holds a modest lead over ggml (rank #103) on the overall Smoower ecosystem score (75 vs 55). The gap of 20 points reflects composite signals across code, docs, community and reach.

On code quality (the state of repositories, tests, releases and polish), Hugging Face is clearly ahead of ggml. On education (docs, guides and learning material for developers), Hugging Face is slightly ahead of ggml. On community (issue response, PR reviews and discussion health), Hugging Face is slightly ahead of ggml. On reach (how visible the ecosystem is beyond its own repos), ggml is clearly ahead of Hugging Face. On momentum (release cadence and how fast the ecosystem moves), Hugging Face is clearly ahead of ggml.

ggml carries 191,415 GitHub stars across 22 public repos, with 15 repositories active in the last 90 days and 308 external contributors on record. Hugging Face shows 642,333 stars across 454 public repos, 80 active in the last 90 days and 862 external contributors. The star gap on its own does not decide the comparison, but Hugging Face's footprint is roughly 3.4x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

ggml is the stronger read for anyone weighting reach. Hugging Face looks better where momentum is the deciding factor. The table below breaks the scores down pillar by pillar; the linked profiles cover the underlying repos, docs and community signals in full.