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lyogavin/airllm
AirLLM 2.8T LLM inference with single 4GB GPU
Current momentum
Stars
39.2K
+1.1K since sync
Commits
7
in period
Active contributors
1
+0%
Issues closed
0
12212.6h 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 activity37.6 / 40 pts
7 commits · 8 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 breadth10.3 / 25 pts
1 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 resolution0.0 / 20 pts
12212.6 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 cadence6.0 / 15 pts
13 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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4K forks
Downstream adoption
167 open issues
Community demand
13 day cadence
Shipping consistency