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vLLM vs Ollama

Compare vLLM and Ollama on deployment, pricing, model support, and more.

vLLM

Tagline
High-throughput LLM inference server with continuous batching and PagedAttention
Description
vLLM is an open-source, high-throughput inference engine for large language models. Its PagedAttention algorithm manages GPU memory efficiently, enabling 2–24× higher throughput than HuggingFace Transformers. It serves an OpenAI-compatible API, making it a drop-in replacement for production LLM serving.
Category
LLM Frameworks
Pricing
Free
Metric
91,490 GitHub stars (source)
Link
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Ollama

Tagline
Run LLMs locally — pull and run Llama, Mistral, Gemma, and 100+ models with one command and OpenAI-compatible API
Description
Ollama is the easiest way to run large language models locally on macOS, Linux, and Windows. With a simple CLI (`ollama pull llama3.1` → `ollama run llama3.1`), it manages model downloads, hardware configuration, and inference. Ollama's local REST API is compatible with the OpenAI API format, making it a drop-in replacement for cloud LLMs in development and privacy-sensitive production deployments. 163K+ GitHub stars; the most popular local LLM runtime.
Category
LLM Frameworks
Pricing
Free
Metric
180,646 GitHub stars (source)
Link
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