Continuous Batching Explained: How vLLM Maximizes Throughput

Introduction to Continuous Batching

Continuous batching, also known as dynamic batching or iteration-level batching, is an advanced scheduling technique used in large language model (LLM) inference. Unlike traditional static batching, which processes a fixed group of requests from start to finish, continuous batching dynamically injects new requests into the active batch as soon as older requests complete. This ensures that the GPU is always operating at maximum capacity, drastically improving throughput and reducing latency.

The Problem with Traditional Batching

In a traditional static batching system, the server waits to collect a certain number of requests, groups them together, and sends them to the GPU. The problem arises because LLM generation lengths are highly variable. If one request generates 10 tokens and another generates 500 tokens, the entire batch must wait for the 500-token generation to finish. The GPU sits idle waiting for the longest sequence, and memory is wasted on padding shorter sequences to match the longest one.

Why Continuous Batching Matters

Continuous batching solves the inefficiencies of static batching by operating at the iteration (or token) level rather than the request level. At each generation step, the system evaluates the active batch. If a request has finished generating its output, it is immediately evicted from the batch, freeing up its memory. Simultaneously, a new waiting request can be injected into the batch in that exact same step.

Maximized GPU Utilization: The GPU never waits for stragglers. As soon as a slot opens up, it is filled.
Higher Throughput: By keeping the GPU constantly fed with active computations, the total number of tokens generated per second increases significantly.
Lower Latency: Requests do not have to wait for a full batch to form before they begin processing. They can start generating as soon as a slot is available.
Efficient Memory Usage: No padding is required because requests are processed independently within the batch.

How vLLM Implements Continuous Batching

vLLM is a high-throughput and memory-efficient LLM serving engine. It achieves its impressive performance primarily through two intertwined technologies: PagedAttention and continuous batching.

For continuous batching to work efficiently, the system must be able to allocate and deallocate memory for the Key-Value (KV) cache dynamically. vLLM uses PagedAttention, which manages the KV cache much like an operating system manages virtual memory. It breaks the KV cache into fixed-size blocks, allowing vLLM to allocate memory to new requests on the fly and reclaim it instantly when a request finishes. This tight memory management is what makes vLLM's continuous batching so robust.

How to Use vLLM for Continuous Batching

Implementing continuous batching with vLLM is straightforward. You can use it as a Python library for offline inference or spin up an OpenAI-compatible API server for production workloads.

Installation and Setup

First, ensure you have a compatible CUDA environment. You can install vLLM via pip:

pip install vllm

Basic Offline Inference Example

You can use vLLM's LLM engine to process a list of prompts. Under the hood, vLLM will automatically apply continuous batching to handle these prompts efficiently.

from vllm import LLM, SamplingParams

# Initialize the LLM engine with a model of your choice
llm = LLM(model="meta-llama/Llama-2-7b-chat-hf")

# Define sampling parameters
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=200)

# Create a list of prompts
prompts = [
    "Explain the concept of continuous batching in one sentence.",
    "Write a Python function to calculate the Fibonacci sequence.",
    "What are the benefits of using vLLM for LLM serving?",
    "Translate 'Hello, world' into French, Spanish, and Japanese."
]

# Generate outputs
# vLLM automatically batches these requests continuously
outputs = llm.generate(prompts, sampling_params)

# Print the outputs
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt}")
    print(f"Generated text: {generated_text}\n")

Serving with vLLM's OpenAI-Compatible Server

For production environments, you will want to run vLLM as an API server. The server natively handles continuous batching for incoming HTTP requests, managing the queue and GPU memory automatically.

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --tensor-parallel-size 1 \
    --port 8000

Once the server is running, you can send requests to it just like you would to the OpenAI API. The server will continuously batch these requests as they arrive.

curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Llama-2-7b-chat-hf",
    "prompt": "Write a short story about a robot learning to paint.",
    "max_tokens": 150,
    "temperature": 0.7
  }'

Best Practices for Maximizing Throughput

To get the most out of vLLM's continuous batching, consider the following best practices:

Monitor KV Cache Usage: vLLM exposes metrics for KV cache utilization. If your cache is consistently full, your GPU is likely bottlenecked by memory rather than compute. Consider reducing the maximum sequence length or using a smaller model.
Tune Tensor Parallelism: If you have multiple GPUs, use the --tensor-parallel-size flag to split the model across them. This increases available memory for the KV cache, allowing more requests to be batched simultaneously.
Set Appropriate Max Tokens: While continuous batching handles variable lengths well, setting a reasonable max_tokens limit prevents runaway generations from hogging KV cache blocks indefinitely.
Batch Your API Requests: If you are using the API server, send multiple prompts in a single request payload where possible. While vLLM handles individual requests well, batching them client-side reduces network overhead.

Conclusion

Continuous batching represents a massive leap forward in the efficiency of LLM inference. By abandoning the rigid constraints of static batching, systems can keep GPUs fully utilized, dramatically increasing throughput and lowering latency for end-users. vLLM leverages this technique alongside PagedAttention to provide a highly optimized, production-ready serving engine. By understanding how continuous batching works and following best practices for memory and parallelism management, developers can serve large language models at scale without breaking the bank on compute costs.