TabbyML vs vLLM

TabbyML and vLLM both appear on the Smoower Developer Ecosystem Index.

vLLM (rank #47) holds a meaningful lead over TabbyML (rank #569) on the overall Smoower ecosystem score (61 vs 35). The gap of 26 points reflects composite signals across code, docs, community and reach.

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

TabbyML carries 34,010 GitHub stars across 18 public repos, with 5 repositories active in the last 90 days and 12 external contributors on record. vLLM shows 115,730 stars across 43 public repos, 35 active in the last 90 days and 881 external contributors. The star gap on its own does not decide the comparison, but vLLM's footprint is roughly 3.4x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

TabbyML is the stronger read for anyone weighting reach. vLLM looks better where code quality 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.