10 things you should know about vLLM

(in 2 mins)

1. vLLM is an open-source LLM inference engine with 76K+ GitHub stars. It's used in production by Meta, LinkedIn, Amazon, and HuggingFace.

2. It uses PagedAttention - which manages GPU memory like your OS manages RAM. This alone reduces memory waste from 60-80% down to under 4% when hosting LLMs.

3. It can serve LLMs up to 24x faster than HuggingFace Transformers. Same model, same GPU - just a smarter serving layer.

4. It uses continuous batching instead of waiting for one request to finish. New requests get added every decoding step so your GPU never sits idle.

5. It exposes an OpenAI-compatible API out of the box. You can swap OpenAI for a self-hosted model without changing your code.

6. It supports quantization formats like GPTQ, AWQ, INT4, INT8, and FP8. This lets you run bigger models on smaller GPUs without major quality loss.

7. It supports prefix caching for repeated prompts. If 1000 users send the same system prompt, the KV cache is computed once.

8. It runs on almost any hardware - NVIDIA, AMD, Intel, TPUs, even Arm CPUs. You're not locked into one vendor.

9. It supports Multi-LoRA serving from a single model instance. One GPU can serve multiple fine-tuned models at the same time.

10. It supports speculative decoding to speed up token generation. The model predicts multiple tokens ahead and verifies them in one pass.

If you're serving LLMs without vLLM

you're probably overpaying for compute.

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