ggml vs vLLM

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

ggml (rank #49) holds a narrow lead over vLLM (rank #85) on the overall Smoower ecosystem score (62 vs 58). The gap of 4 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 ggml. On education (docs, guides and learning material for developers), ggml is ahead of vLLM. On community (issue response, PR reviews and discussion health), ggml is slightly ahead of vLLM. On reach (how visible the ecosystem is beyond its own repos), ggml is clearly ahead of vLLM. On momentum (release cadence and how fast the ecosystem moves), vLLM is ahead of ggml.

ggml carries 208,448 GitHub stars across 26 public repos, with 18 repositories active in the last 90 days and 301 external contributors on record. vLLM shows 131,658 stars across 52 public repos, 43 active in the last 90 days and 1,044 external contributors.

ggml 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.