vLLM

High-throughput LLM serving engine with PagedAttention for production deployments.

Quick Facts

Attribute	Value
Pricing	Free
License	Apache 2.0
Best For	Production serving, high throughput
Language	Python
Key Innovation	PagedAttention
Founded	2023

What is vLLM?

vLLM is an inference engine optimized for serving LLMs at scale. While Ollama and LM Studio target personal use, vLLM is designed for production deployments handling many concurrent requests.

The key innovation is PagedAttention—a memory management technique that dramatically improves throughput when serving multiple users. This makes vLLM 2-4x faster than HuggingFace Transformers for typical serving workloads.

Key Features

• PagedAttention - Efficient KV cache memory management
• High Throughput - Optimized for concurrent requests
• Continuous Batching - Dynamic request batching
• Quantization - AWQ, GPTQ, SqueezeLLM support
• OpenAI-Compatible - Drop-in API replacement
• Tensor Parallelism - Multi-GPU support
• Streaming - Token-by-token output
• Model Hub - Direct HuggingFace integration

Installation

pip install vllm

# Start server
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-4-8B \
  --port 8000

# Or use Docker
docker run -p 8000:8000 vllm/vllm-openai \
  --model meta-llama/Llama-4-8B

Performance Comparison

Metric	vLLM	HF Transformers
Throughput	14x higher	Baseline
Latency	2-4x lower	Baseline
Memory	More efficient	Standard

Benchmarks vary by model and hardware.

Pros & Cons

Pros:

• Best throughput for serving
• Production-ready
• OpenAI-compatible API
• Multi-GPU support
• Active development

Cons:

• Requires more setup than Ollama
• Python-only
• Overkill for personal use
• Higher resource requirements

Alternatives

• Ollama - Personal use, simpler
• llama.cpp - Lower resource usage
• TGI - HuggingFace’s serving solution

FAQ

When should I use vLLM vs Ollama? Use vLLM for production serving with multiple users. Use Ollama for personal use and development.

Does vLLM work on CPU? No, vLLM requires NVIDIA GPUs. For CPU inference, use llama.cpp or Ollama.

What’s PagedAttention? A memory management technique that stores KV cache in non-contiguous blocks, reducing memory waste and increasing throughput.

Can vLLM run quantized models? Yes, vLLM supports AWQ and GPTQ quantization formats.

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Last verified: 2026-03-04