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vLLM

Definition

A high-throughput LLM serving library that uses PagedAttention for efficient memory management, enabling faster inference and higher GPU utilization in production.

vLLM is an open-source library for high-throughput LLM inference and serving, using a novel memory management technique called PagedAttention to dramatically improve efficiency.

Why It Matters

Traditional LLM inference wastes significant GPU memory due to inefficient key-value cache allocation. vLLM solves this with PagedAttention, which manages memory like virtual memory in operating systems. This delivers:

  • 2-24x higher throughput compared to naive implementations
  • Lower latency per request in high-load scenarios
  • Better GPU utilization for production deployments
  • Cost reduction by serving more requests per GPU

For AI engineers building production systems, vLLM is often the difference between needing one GPU or four to handle the same load.

Implementation Basics

Key features:

  1. PagedAttention: Allocates KV cache in non-contiguous blocks, reducing memory waste from 60-80% to near zero
  2. Continuous batching: Dynamically adds new requests to running batches for optimal throughput
  3. Tensor parallelism: Distribute large models across multiple GPUs
  4. OpenAI-compatible API: Drop-in replacement for OpenAI API endpoints
  5. Quantization support: AWQ, GPTQ, and other quantization formats

Basic deployment:

# Start vLLM server
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3-8B-Instruct \
    --dtype auto

vLLM is particularly valuable for serving open-source models in production, where you need to maximize throughput while minimizing infrastructure costs. It’s become the standard for self-hosted LLM inference at scale.

Source

vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention

https://arxiv.org/abs/2309.06180

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