Ollama vs vLLM: which one to choose in 2026?
Both are open source alternatives to OpenAI API. We compare license, tech stack and features to help you decide which fits your team best.
| Ollama | vLLM | |
|---|---|---|
| Category | Artificial Intelligence | Artificial Intelligence |
| License | MIT | Apache-2.0 |
| GitHub stars | ~99k | ~29k |
| Tech stack | Go, Llama.cpp | Python, CUDA |
| Replaces | OpenAI API | OpenAI API |
Ollama has more GitHub stars, which usually indicates a larger community (not necessarily that it's the best option for your use case).
Ollama
Run open source LLMs locally, an alternative to the OpenAI API.
ℹ️ CPU-only is viable for models ≤7B (needs ~8GB of free RAM). For 13B+ models or fast inference, a dedicated GPU with at least 8-12GB VRAM is recommended.
Pros
- Your data never leaves your server
Things to consider
- Quality depends on the model and hardware available
vLLM
High-performance LLM inference engine, an alternative to the OpenAI API in production.
Pros
- Purpose-built for serving LLMs in production at scale
Things to consider
- Requires a GPU with enough VRAM for the chosen model
Key features
Ollama
- Download models with a single command
- API compatible with multiple clients
- GPU and CPU support
vLLM
- Much higher throughput thanks to PagedAttention
- OpenAI-compatible API
- Supports dozens of model architectures
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