© 2026 AI-Buzz. Early access — data updated daily.
© 2026 AI-Buzz. Early access — data updated daily.
Fast LLM inference engine. High-throughput serving for LLMs.
Metrics computed from HN discussion, GitHub activity, and funding data.
According to AI-Buzz, vLLM ranks #4 in AI Infrastructure for HN discussion share (out of 70 tracked with 14.1% of HN discussion), with 46% positive developer sentiment (100 HN comments analyzed), with 170 commits/week (declining), with 4,433,727 PyPI downloads in 30 days.
Source: https://ai-buzz.com/companies/vllm?utm_source=citation&utm_medium=referral&utm_campaign=cite_this_data
Metrics derived from public APIs (HN Algolia, GitHub, npm/PyPI). Sentiment classified by AI. See methodology for details →
Description
Fast LLM inference engine. High-throughput serving for LLMs.
Estimated Company Size
11 - 50 employees
Website
vllm.aiFounded
2023
Description
vLLM is a fast LLM inference engine. High-throughput serving for LLMs. Used by many AI applications.
Community engagement metrics that indicate developer traction and interest.
Last updated: 1 day ago
Mentions in HN discussions. Source: Hacker News Algolia API.
Sentiment analysis of Hacker News comments only. Does not include Reddit, Discord, or other platforms.
Total stars indicate project popularity and developer adoption.
Forks indicate active developer engagement and contribution interest.
Weekly commit activity indicates ongoing development momentum and team productivity.
Package download volume indicates real-world adoption and integration into production projects.
An open-source library for efficient LLM serving and inference, utilizing PagedAttention for high throughput and low latency.
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