How to Intermediate · 3 min read

How continuous batching works in vLLM

Quick answer

In vLLM, continuous batching dynamically accumulates incoming requests into batches to maximize GPU throughput without waiting for fixed batch sizes. This approach reduces latency and improves resource utilization by immediately processing batches once they reach a threshold or after a timeout, enabling efficient large language model inference.

PREREQUISITES

Python 3.8+
pip install vllm
Access to a GPU-enabled environment

Setup

Install the vllm package and ensure you have a GPU environment ready for inference.

bash

pip install vllm

Step by step

Use vLLM to create a server that supports continuous batching by default. Incoming requests are queued and dynamically batched based on batch size or timeout thresholds, maximizing GPU utilization.

python

from vllm import LLM, SamplingParams

# Initialize the LLM with a model
llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct")

# Define prompts to simulate incoming requests
prompts = [
    "Explain continuous batching in vLLM.",
    "What is the benefit of dynamic batching?",
    "How does vLLM reduce latency?"
]

# Generate outputs with continuous batching handled internally
outputs = llm.generate(prompts, SamplingParams(temperature=0.7))

for i, output in enumerate(outputs):
    print(f"Response {i+1}:", output.outputs[0].text.strip())

output

Response 1: Continuous batching in vLLM dynamically groups incoming requests to maximize GPU throughput.
Response 2: Dynamic batching improves efficiency by reducing idle GPU time and lowering latency.
Response 3: vLLM reduces latency by processing batches as soon as they reach a threshold or timeout.

Common variations

You can customize batching behavior by adjusting batch size and timeout parameters in the vLLM server CLI or API. Async usage is supported by querying a running vLLM server via the OpenAI-compatible API with continuous batching enabled.

python

# CLI command to start vLLM server with batch size and timeout
# vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000 --max-batch-size 16 --max-batch-wait-ms 10

# Python client querying the running server
from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"], base_url="http://localhost:8000/v1")

response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "Explain continuous batching in vLLM."}]
)
print(response.choices[0].message.content)

output

Continuous batching in vLLM dynamically groups incoming requests to maximize GPU throughput and reduce latency.

Troubleshooting

If latency is high, verify your batch size and timeout settings to ensure batches form quickly without excessive waiting. Ensure your GPU has sufficient memory for the configured batch size to avoid out-of-memory errors. If requests are processed one-by-one, confirm that the server is running with continuous batching enabled and clients are sending requests concurrently.

✅

Key Takeaways

Continuous batching in vLLM dynamically groups requests to maximize GPU efficiency and reduce latency.
Batch size and timeout parameters control how quickly batches form and are processed.
vLLM supports both direct Python usage and OpenAI-compatible API querying for continuous batching.
Proper GPU memory sizing is critical to handle larger batch sizes without errors.
Monitoring latency and throughput helps optimize batching parameters for your workload.

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

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