AI developer tools
vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Current momentum
Stars
94.3K
+277 since sync
Commits
100
in period
Active contributors
62
+0%
Issues closed
0
168h avg. resolution
Recorded trajectory
Movement you can verify over time.
Loading daily momentum history.
Behind the number
A score you can inspect.
Each signal contributes points to the total. These are capped GitHub samples, not complete activity counts or proof of adoption.
Development activity40.0 / 40 pts
100 commits · 58 opened PRs · 0 closed issues in 7 days
24 + 22 × ln(1 + weekly weighted activity). Activity = commits + 2 × opened PRs + closed issues. Signal bounded to 0–100, then weighted 40%.
Contributor breadth25.0 / 25 pts
62 sampled contributors in 7 days · 0% contributor-growth input
24 + 25 × ln(1 + contributors × √(7 / days)) + 0.15 × growth. Growth uses the 30-day windows; capped samples use a neutral 0 input. Signal bounded to 0–100, then weighted 25%.
Issue resolution7.7 / 20 pts
168 hours resolution input
105 − 9 × log₂(1 + hours). Uses sampled closed issues, not first-response time. With no resolved issues, the model uses 168 hours. Signal bounded to 0–100, then weighted 20%.
Release cadence6.8 / 15 pts
12 days cadence input
105 − 5 × cadence days. With fewer than two published releases, the model uses time since the last push as a proxy. Signal bounded to 0–100, then weighted 15%.
What changed in the recorded sample?
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Local and open AI stack alternatives
Evaluate 4 related open-source projects.
Open a bounded 30-day shortlist with the same activity, contributor, response, and release lens—plus explicit fit questions before you choose.
23.6K forks
Downstream adoption
9.2K open issues
Community demand
12 day cadence
Shipping consistency