rtp-llm vs vllm

Common questions

What is the difference between rtp-llm and vllm?

rtp-llm: Alibaba's high-performance LLM inference engine for diverse applications.. vllm: A high-throughput and memory-efficient inference and serving engine for LLMs. See the comparison table for live GitHub stats and shared categories.

When should I choose rtp-llm over vllm?

Choose rtp-llm over vllm when Requirements: Requires CUDA configuration and NVIDIA GPU availability to exploit its full performance capabilities.; Both RTP-LLM and vllm are high-performance LLM inference engines designed for efficient running of large language models, offering similar functionality but with different implementations; Tags unique to rtp-llm: llama, llm, llm-serving, llmops; When you are looking for a tool that leverages CUDA-based optimization for deploying Large Language Models (LLMs) across diverse applications.

When should I choose vllm over rtp-llm?

Choose vllm over rtp-llm when Requirements: Min 8 GB RAM; Installation can be done via uv pip install vllm or building from source for development purposes.; Both RTP-LLM and vllm are high-performance LLM inference engines designed for efficient running of large language models, offering similar functionality but with different implementations; Tags unique to vllm: amd, blackwell, cuda, deepseek; When you need a high-throughput and memory-efficient inference engine for large language models, especially if you are working with a variety of hardware configurations including AMD, CUDA, and TPU.

When should I avoid rtp-llm?

When your development environment does not support CUDA as RTP-LLM is primarily based on it and might perform inadequately without direct GPU-acceleration from NVIDIA. If your project has strict licensing constraints; while the Apache-2.0 license is permissive, certain projects may require tools with different or more restrictive licenses to meet compliance needs. For applications that do not align well with Alibaba’s backend setup and prefer a more independent or competitor-based solution for their LLM serving needs.

When should I avoid vllm?

If your project strictly requires a specific competitor's ecosystem or features that are not covered by vLLM, such as specialized model serving capabilities not supported by vLLM. When the project's hardware setup is incompatible with the supported hardware configurations of vLLM, such as certain non-supported GPU models or configurations.

Is rtp-llm or vllm more popular on GitHub?

vllm has more GitHub stars (92,075 vs 1,341). Stars measure visibility, not whether either tool fits your constraints.

Are rtp-llm and vllm open source?

Yes - both are open-source projects on GitHub (rtp-llm: Apache-2.0, vllm: Apache-2.0).

Where can I find alternatives to rtp-llm or vllm?

GraphCanon lists graph-backed alternatives at rtp-llm alternatives and vllm alternatives (rtp-llm markdown twin, vllm markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, rtp-llm or vllm?

rtp-llm: Very active. vllm: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for rtp-llm and vllm?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: rtp-llm trust report; vllm trust report.