vLLM — High-Throughput LLM Serving Engine

What it is

vLLM is a high-throughput, memory-efficient inference engine for serving large language models. Its key innovation is PagedAttention, which manages attention key-value caches like virtual memory pages, dramatically reducing memory waste and enabling higher concurrent request handling. It provides an OpenAI-compatible API server, continuous batching, multi-GPU tensor parallelism, and support for a wide range of model architectures.

It targets teams deploying LLMs to production who need maximum throughput and minimum latency per dollar of compute.

How it saves time or tokens

vLLM's PagedAttention eliminates up to 90% of the memory waste in traditional attention KV cache management. This means you serve more concurrent users on the same GPU hardware. Continuous batching ensures the GPU stays saturated -- new requests are added to the batch immediately rather than waiting for the current batch to complete. The result is 2-4x higher throughput compared to naive serving approaches.

How to use

1. Install:

pip install vllm

1. Serve a model with OpenAI-compatible API:

vllm serve meta-llama/Llama-3.1-8B-Instruct

1. Query like any OpenAI API:

from openai import OpenAI

client = OpenAI(base_url='http://localhost:8000/v1', api_key='dummy')

response = client.chat.completions.create(
    model='meta-llama/Llama-3.1-8B-Instruct',
    messages=[{'role': 'user', 'content': 'Explain PagedAttention briefly.'}],
)
print(response.choices[0].message.content)

1. For multi-GPU:

vllm serve meta-llama/Llama-3.1-70B-Instruct --tensor-parallel-size 4

Example

Related on TokRepo

• Local LLM: vLLM -- vLLM deep-dive on TokRepo
• Local LLM tools -- all local LLM tools

Common pitfalls

• vLLM requires NVIDIA GPUs with CUDA. AMD ROCm support exists but is less mature. Apple Silicon and CPU-only inference are not supported -- use llama.cpp or Ollama for those platforms.
• Model loading requires enough GPU memory to hold the model weights plus KV cache. Check memory requirements before deploying. Quantized models reduce the memory footprint.
• The OpenAI-compatible API covers most endpoints but may not support every feature of the official OpenAI API. Test your specific use case.