ggml vs TabbyML

ggml (AI inference at the edge) and TabbyML both appear on the Smoower Developer Ecosystem Index.

ggml (rank #103) holds a modest lead over TabbyML (rank #569) on the overall Smoower ecosystem score (55 vs 35). 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), ggml is ahead of TabbyML. On education (docs, guides and learning material for developers), ggml is clearly ahead of TabbyML. On community (issue response, PR reviews and discussion health), ggml is clearly ahead of TabbyML. On reach (how visible the ecosystem is beyond its own repos), ggml is clearly ahead of TabbyML. On momentum (release cadence and how fast the ecosystem moves), ggml is ahead of TabbyML.

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. TabbyML shows 34,010 stars across 18 public repos, 5 active in the last 90 days and 12 external contributors. The star gap on its own does not decide the comparison, but ggml's footprint is roughly 5.6x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

ggml is the stronger read for anyone weighting reach. TabbyML makes more sense for teams already using its adjacent tools. The table below breaks the scores down pillar by pillar; the linked profiles cover the underlying repos, docs and community signals in full.