High-throughput and memory-efficient inference and serving engine for Large Language Models. Deploy AI faster with state-of-the-art performance.
I notice that the reviews section is empty and the social mentions only show YouTube video titles that simply repeat "vLLM AI" without any actual user feedback or review content. Without substantive user reviews, comments, or detailed social media discussions to analyze, I cannot provide a meaningful summary of what users think about vLLM's strengths, complaints, pricing sentiment, or overall reputation. To give you an accurate assessment, I would need actual user feedback, reviews with ratings/comments, or social media posts that contain users' opinions and experiences with the tool.
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14,991 forks
I notice that the reviews section is empty and the social mentions only show YouTube video titles that simply repeat "vLLM AI" without any actual user feedback or review content. Without substantive user reviews, comments, or detailed social media discussions to analyze, I cannot provide a meaningful summary of what users think about vLLM's strengths, complaints, pricing sentiment, or overall reputation. To give you an accurate assessment, I would need actual user feedback, reviews with ratings/comments, or social media posts that contain users' opinions and experiences with the tool.
Features
Industry
information technology & services
Employees
21
2,937
GitHub followers
36
GitHub repos
74,806
GitHub stars
20
npm packages
1
HuggingFace models
Repository Audit Available
Deep analysis of vllm-project/vllm — architecture, costs, security, dependencies & more
vLLM uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Cash Donations, Compute Resources, Slack Sponsor, Hardware, Open Models, Recipes, Performance, Roadmap.
vLLM has a public GitHub repository with 74,806 stars.
Philipp Schmid
Tech Lead at Hugging Face
2 mentions