vLLM vs TGI for self-hosted LLM API endpoints

vLLM vs TGI comparison for self-hosted LLM APIs

When one person chatting becomes ten people hitting an API, llama.cpp stops being the right tool. That is where vLLM and Text Generation Inference (TGI) come in. Both do continuous batching: they pack multiple requests into each graphics processing unit (GPU) pass, which multiplies throughput.

vLLM is the community favorite. It supports the widest range of models, uses PagedAttention for efficient key-value cache handling, and the ecosystem around it is large. TGI is the Hugging Face server. If your models come from the Hugging Face Hub and you like that toolchain, TGI feels native, and its structured generation support is solid.

For most self-hosters the practical difference is smaller than the documentation suggests. Choose vLLM for breadth, TGI if you live in the Hugging Face world. Either way, serving makes video memory (VRAM) math serious: batch size multiplies memory needs, so measure with your real concurrency instead of a single-user benchmark. Size a serving setup with the VRAM calculator.

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