Which is more popular, MinerU or vLLM?

vLLM has more GitHub stars (93,014 vs 80,939).

Which is more actively developed, MinerU or vLLM?

vLLM had more commits in the last 90 days (3,946 vs 911).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Pick MinerU for: transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows. Pick vLLM for: a high-throughput and memory-efficient inference and serving engine for LLMs.

From GitHub data refreshed daily.

Transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows.

vLLMopen-source

A high-throughput and memory-efficient inference and serving engine for LLMs

+Exceptional serving throughput with PagedAttention memory optimization and continuous batching for production-scale LLM deployment
+Comprehensive hardware support across NVIDIA, AMD, Intel platforms and specialized accelerators with flexible parallelism options
+Seamless Hugging Face integration with OpenAI-compatible API server for easy model deployment and switching

-Requires significant GPU memory for optimal performance, limiting accessibility for resource-constrained environments
-Complex setup and configuration for distributed inference across multiple GPUs or nodes
-Primary focus on inference means limited support for training or fine-tuning workflows

•Production API serving for applications requiring high-throughput LLM inference with multiple concurrent users
•Research and experimentation with open-source LLMs requiring efficient model switching and testing
•Enterprise deployment of private LLM services with OpenAI-compatible interfaces for existing applications

Which is more popular, MinerU or vLLM?: vLLM has more GitHub stars (93,014 vs 80,939).
Which is more actively developed, MinerU or vLLM?: vLLM had more commits in the last 90 days (3,946 vs 911).
Should I use MinerU or vLLM?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.