vllm-project/vllm: High-Throughput & Memory-Efficient LLM Serving Engine

vLLM is an open-source, high-throughput, and memory-efficient LLM serving and inference engine. By introducing PagedAttention, vLLM redefines Key-Value (KV) cache management in Transformer models, solving the primary bottleneck in high-concurrency LLM deployment.

The Core Bottleneck: KV Cache Fragmentation

In traditional Transformer serving engines, Key-Value cache memory is pre-allocated contiguously in GPU memory. This approach leads to severe internal and external memory fragmentation (often wasting 60–80% of available VRAM). As a result, batch sizes remain small, GPU utilization stays low, and request latency spikes under load.

The Architectural Solution: PagedAttention

vLLM addresses memory fragmentation by borrowing virtual memory paging concepts from modern operating systems:

Paged KV Cache: KV cache is divided into fixed-size physical blocks (e.g., 16 tokens per block).
Virtual Block Mapping: Logical token blocks map non-contiguously to physical GPU memory blocks.
Dynamic Memory Allocation: Blocks are allocated on demand as tokens are generated, eliminating pre-allocation waste.
Copy-on-Write Sharing: Multiple parallel decoding branches (such as beam search or multi-candidate generation) share physical blocks until a mutation occurs.

Benchmarks & Performance Gains

Under heavy multi-user workloads, vLLM achieves:

2x–4x higher throughput compared to HuggingFace TGI (Text Generation Inference).
Up to 24x throughput improvements over standard PyTorch baseline implementations.
Sub-millisecond TTFT (Time To First Token) even under high batch concurrency.

Local & Server Installation

Installing vLLM with CUDA 12 support:


pip install vllm

Launching an OpenAI-compatible API server serving Meta Llama-3-8B-Instruct:


python3 -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3-8B-Instruct \
    --port 8000 \
    --gpu-memory-utilization 0.90

Querying the local API endpoint:


curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Meta-Llama-3-8B-Instruct",
    "messages": [{"role": "user", "content": "Explain PagedAttention in three concise sentences."}]
  }'

Key Takeaway

For teams deploying open-source LLMs into production, vLLM provides the highest throughput per GPU dollar available today.