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vLLM Alternatives

High-throughput LLM inference engine with PagedAttention for efficient GPU memory usage

vLLM is an open-source inference and serving engine for Large Language Models, originally developed at UC Berkeley.

Explore 90 alternatives to vLLM across 2 categories. Each tool listed below shares at least one category with vLLM.

Top vLLM alternatives at a glance

  1. llama.cpp. LLM inference in C/C++ with broad hardware support and aggressive quantization
  2. Modular. We rebuilt the modern AI software stack, from the ground up, to boost any AI pipeline, on any hardware.
  3. Ollama. Run large language models locally with a single command
  4. LangChain. LangChain gives developers a framework to construct LLM‑powered apps easily.
  5. Dify. Easily build and operate generative AI applications. Create Assistants API and GPTs based on any LLMs.

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Frequently asked questions

What are the best alternatives to vLLM?

Based on category overlap and popularity, the top alternatives to vLLM include: llama.cpp (LLM inference in C/C++ with broad hardware support and aggressive quantization); Modular (We rebuilt the modern AI software stack, from the ground up, to boost any AI ...); Ollama (Run large language models locally with a single command); LangChain (LangChain gives developers a framework to construct LLM‑powered apps easily.); Dify (Easily build and operate generative AI applications. Create Assistants API ...). See all 90 alternatives compared on this page.

Is there a free alternative to vLLM?

Yes. 53 alternatives to vLLM offer a free tier or free trial: llama.cpp, Modular, LangChain, Dify, GPT4All, LiteLLM, and more. Use the comparison above to find the best fit for your use case.

Are there open-source alternatives to vLLM?

Yes. 28 open-source alternatives to vLLM are listed here: llama.cpp, Ollama, LangChain, Dify, GPT4All, LiteLLM, and more. Open-source tools can be self-hosted for full control over data and infrastructure.

What is vLLM?

vLLM is an open-source inference and serving engine for Large Language Models, originally developed at UC Berkeley. It uses PagedAttention to manage GPU memory efficiently, achieving up to 24x higher throughput compared to Hugging Face Transformers. It supports most popular open-source models inc... See 90 alternatives to vLLM across 2 categories.

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