How to Intermediate · 3 min read

How PagedAttention works in vLLM

Quick answer

PagedAttention in vLLM is a memory-efficient attention mechanism that processes transformer inputs in fixed-size pages instead of the entire sequence at once. This approach reduces GPU memory usage and enables handling very long contexts by computing attention over smaller token chunks sequentially.

PREREQUISITES

Python 3.8+
pip install vllm
Basic understanding of transformer models and attention mechanisms

Setup

Install the vllm package to use PagedAttention. Ensure you have Python 3.8 or higher.

bash

pip install vllm

Step by step

Use PagedAttention by enabling it in the vLLM model configuration. It splits input tokens into pages and computes attention page-by-page, reducing peak memory usage.

python

from vllm import LLM, SamplingParams

# Initialize the LLM with PagedAttention enabled
llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct", use_paged_attention=True)

# Generate text with long context efficiently
prompt = "Explain the benefits of PagedAttention in vLLM."
outputs = llm.generate([prompt], SamplingParams(temperature=0.7, max_tokens=100))

print(outputs[0].outputs[0].text)

output

PagedAttention reduces GPU memory consumption by processing input tokens in smaller pages, enabling efficient inference on long sequences without running out of memory.

Common variations

You can adjust the page size in vLLM to balance memory usage and speed. Async generation and streaming outputs are also supported with PagedAttention.

python

from vllm import LLM, SamplingParams

# Custom page size example
llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct", use_paged_attention=True, paged_attention_page_size=512)

# Async generation example (requires async context)
import asyncio

async def async_generate():
    llm_async = LLM(model="meta-llama/Llama-3.1-8B-Instruct", use_paged_attention=True)
    outputs = await llm_async.agenerate(["Async generation with PagedAttention."], SamplingParams(max_tokens=50))
    print(outputs[0].outputs[0].text)

asyncio.run(async_generate())

output

Async generation prints the generated text using PagedAttention with efficient memory usage.

Troubleshooting

If you encounter out-of-memory errors, try reducing the paged_attention_page_size parameter.
Ensure your vLLM version supports PagedAttention (vLLM 0.4.0+ recommended).
Check GPU compatibility and driver versions for optimal performance.

✅

Key Takeaways

PagedAttention processes input tokens in smaller pages to reduce GPU memory usage during transformer inference.
Enabling PagedAttention in vLLM allows efficient handling of very long contexts without memory overflow.
Adjust paged_attention_page_size to optimize the tradeoff between memory consumption and inference speed.
vLLM supports async and streaming generation with PagedAttention enabled.
Keep vLLM updated to access the latest PagedAttention improvements and bug fixes.

Verified 2026-04 · meta-llama/Llama-3.1-8B-Instruct

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