vLLM: High-Throughput Distributed LLM Serving Engine

What is vLLM?

vLLM is a high-performance open-source inference and serving engine designed for LLMs. Created at UC Berkeley, it introduced PagedAttention, an algorithm that treats the Key-Value (KV) cache as virtual memory pages, reducing memory waste from over 60% down to under 4%.

This architectural breakthrough allows vLLM to dynamically batch multiple incoming generation requests at the token level, delivering enterprise-scale throughput on modern GPUs.

Key Architectural Capabilities

• PagedAttention: Virtual memory allocation for KV cache tensors eliminates fragmentation and enables efficient memory sharing across parallel requests.
• Continuous Batching: Requests are scheduled at token iterations rather than waiting for entire sequences to finish, maximizing GPU compute utilization.
• Chunked Prefill: Long prompt prefill computations are divided into smaller chunks and interleaved with decoding steps to protect tail latency.
• Tensor Parallelism: Seamlessly shard models across 2, 4, or 8 GPUs on single or multi-node clusters.
• Quantization Support: Native FP8, AWQ, GPTQ, and Marlin kernels for accelerated inference with lower memory overhead.

Installation & Setup

vLLM requires Linux with an NVIDIA GPU (Compute Capability 7.0+) or AMD ROCm support:

# Recommended: Create an isolated environment
python3 -m venv vllm-env
source vllm-env/bin/activate

# Install vLLM with CUDA 12 support
pip install --upgrade pip
pip install vllm

Production Deployment Recipes

1. Launch OpenAI-Compatible Model Server

python3 -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --port 8000 \
  --gpu-memory-utilization 0.90 \
  --max-model-len 8192 \
  --enforce-eager

2. Multi-GPU Tensor Parallelism (e.g. 2x RTX 4090 / 2x A100)

python3 -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3.1-70B-Instruct \
  --tensor-parallel-size 2 \
  --quantization awq \
  --port 8000

3. Docker Container Deployment

docker run --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 8000:8000 \
  --ipc=host \
  vllm/vllm-openai:latest \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --gpu-memory-utilization 0.90