vLLM is an open-source library for fast LLM inference and serving. It uses PagedAttention to manage GPU memory efficiently, achieving 10-24x higher throughput than naive implementations like HuggingFace Transformers.

How is vLLM different from HuggingFace Transformers?

HuggingFace Transformers is designed for research and flexibility. vLLM is designed for production serving with maximum throughput. vLLM uses PagedAttention, continuous batching, and optimized CUDA kernels to serve many concurrent requests efficiently.

When should I use vLLM?

Use vLLM when serving LLMs in production with multiple concurrent users, when you need high throughput, or when GPU memory efficiency matters. It is ideal for API servers, chatbots, and batch inference workloads.

What is vLLM? High-Throughput LLM Inference Explained

May 2026 · 7 min read · Infrastructure

vLLM is an open-source library for fast LLM inference and serving. It achieves 10-24x higher throughput than naive HuggingFace inference by using PagedAttention, continuous batching, and optimized memory management.

Why vLLM Exists

The bottleneck in LLM serving is not compute — it is memory. Each request needs a KV cache that grows with sequence length. Naive implementations waste 60-80% of GPU memory on fragmentation. vLLM solves this.

Key Innovations

1. PagedAttention

Inspired by OS virtual memory. Instead of allocating contiguous memory for each sequence, vLLM splits the KV cache into fixed-size blocks (pages) and maps them dynamically. This eliminates memory fragmentation.

2. Continuous Batching

Traditional batching waits for all sequences to finish. vLLM inserts new requests as soon as any sequence completes a token. This keeps the GPU saturated at all times.

3. Efficient Memory Sharing

For parallel sampling (beam search, multiple completions), vLLM shares KV cache pages across sequences using copy-on-write. This reduces memory usage by up to 55%.

Performance Comparison

System	Throughput (req/s)	Memory Efficiency
HuggingFace Transformers	1x (baseline)	~20-40%
TGI (Text Generation Inference)	3-5x	~60%
vLLM	10-24x	~95%

Quick Start

pip install vllm

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-3-8B-Instruct")
params = SamplingParams(temperature=0.7, max_tokens=256)

outputs = llm.generate(["Explain RAG in 3 sentences."], params)
print(outputs[0].outputs[0].text)

Serving as an API

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3-8B-Instruct \
    --port 8000

This gives you an OpenAI-compatible API endpoint. Drop-in replacement for any OpenAI SDK client.

When to Use vLLM

Serving LLMs to multiple concurrent users
Building chatbot APIs or AI assistants
Batch inference on large datasets
Any scenario where throughput and GPU efficiency matter

When NOT to Use vLLM

Single-user local inference (use Ollama or llama.cpp instead)
Models under 1B parameters (overhead not worth it)
Research/experimentation (use HuggingFace for flexibility)

FAQ

What is vLLM?

An open-source library for fast LLM inference using PagedAttention. It achieves 10-24x higher throughput than naive implementations.

Is vLLM free?

Yes, vLLM is open-source under the Apache 2.0 license.

Does vLLM support quantized models?

Yes. vLLM supports AWQ, GPTQ, and FP8 quantization for reduced memory usage and faster inference.

Learn More

Production Inference Course — vLLM, TensorRT, optimization
GGUF Explained — Run LLMs locally
Quantization Engineering Course