Large language models have become easier to integrate into applications, but running them efficiently is still a major engineering challenge. As the number of users and requests increases, inference workloads can consume large amounts of GPU memory and computing resources. An application that works well with a few requests can become expensive or slow when it needs to serve thousands of requests at the same time.
This is where vLLM comes in. vLLM is an open-source library and inference engine designed to make large language model serving faster and more efficient. It focuses on improving GPU utilization, managing the memory required during generation, and processing multiple requests efficiently. It can also expose models through an OpenAI-compatible HTTP API, making it possible to use vLLM as the serving layer behind existing applications.
In this article, we will explain what vLLM is, why efficient LLM inference matters, and how to get started with running a model using vLLM.
1. What is vLLM?
vLLM is an open-source library for LLM inference and serving. It provides an inference engine that manages the computational work required to generate model responses and a server that allows applications to communicate with the model over HTTP. Its main goal is to improve throughput and resource utilization when serving language models.
The name is commonly associated with virtual large language model, although the more important point for developers is what the project does rather than the name itself. Instead of changing the underlying language model, vLLM provides a more efficient way to execute and serve supported models.
This distinction is important because vLLM is not a model such as Llama, Qwen, or Mistral. It is the serving infrastructure that runs a model. You can think of the model as the trained component that generates the answer, while vLLM provides the runtime responsible for handling inference requests efficiently.
For example, an application might send a user’s question to a vLLM server, which then loads a model and performs the required inference on available GPUs. The generated response is returned to the application through the server’s API.
2. Why is LLM Inference Difficult?
When an LLM generates a response, it performs a large number of mathematical operations. The model processes the input prompt and then generates output tokens one at a time. During this process, it maintains information about tokens that have already been processed so that it does not need to repeat the same calculations unnecessarily.
One of the important pieces of this process is the key-value cache, commonly called the KV cache. During the initial processing of a prompt, often referred to as the prefill phase, the model creates key and value information for the input tokens. During the decode phase, when the model generates new tokens, that cached information is reused. As prompts and generated responses become longer, the KV cache can consume a significant amount of GPU memory.
The problem becomes more noticeable when many users are making requests simultaneously. Each request may require its own cache, and the size of those caches can change as generation progresses. Poor memory management can leave GPU memory fragmented or underutilized even when the hardware has substantial capacity.
This means that simply having a powerful GPU does not automatically guarantee efficient LLM serving. The inference engine also needs to manage memory and schedule requests effectively.
3. How Does vLLM Improve LLM Inference?
vLLM addresses these challenges through several optimization techniques. One of its best-known contributions is PagedAttention, which was designed specifically to manage the KV cache more efficiently. Traditional approaches can allocate memory for a sequence in a way that makes it difficult to reuse available space efficiently. PagedAttention takes inspiration from virtual memory systems used by operating systems and divides the KV cache into manageable blocks. This allows vLLM to manage memory more flexibly as requests grow and finish.
The result is better GPU memory utilization and less wasted space. This matters because efficient memory management can allow the server to handle more concurrent requests with the same hardware.
vLLM also uses continuous batching. Instead of waiting for a fixed batch of requests to finish before processing another batch, the inference engine can dynamically add and remove requests as they progress. This keeps the available GPU resources busy while different requests are at different stages of generation.
Other optimizations supported by the project include quantization, tensor parallelism, speculative decoding, CUDA graph optimizations, and efficient attention implementations. The exact benefits depend on the model, hardware, workload, and configuration.
3.1 PagedAttention
An easy way to understand PagedAttention is to think about how memory is allocated. Suppose an inference server is handling several users. One user sends a short question and another sends a very long document. Their KV caches will have different sizes, and those sizes may change while the responses are being generated.
A simple memory allocation strategy can reserve larger contiguous areas than are actually needed. When requests finish, those areas may also leave gaps that are difficult to reuse efficiently.
PagedAttention treats the KV cache more like a collection of smaller memory blocks. These blocks can be allocated and managed independently, allowing the inference engine to make better use of available GPU memory. The approach was introduced in the research behind vLLM and is one of the technologies that helped establish the project as a high-performance LLM serving system.
The important idea is not that PagedAttention makes the language model itself smaller. Instead, it improves how the inference system manages the memory required while the model is running.
3.2 Continuous Batching
Batching is another important concept in vLLM. Traditional batching groups requests together and processes them as a unit. This can work well when requests have similar lengths and finish at roughly the same time, but LLM requests are often unpredictable. One user might request a short answer while another asks the model to generate several thousand tokens.
Continuous batching allows the server to dynamically manage requests as generation progresses. When one request finishes, another can be introduced without waiting for an entire batch to complete. This helps keep the GPU busy and increases overall throughput.
This is especially relevant for applications such as chatbots, AI assistants, recommendation systems, document processing, and agentic applications where many inference requests may arrive concurrently.
3.3 vLLM and Throughput
One of the main metrics when evaluating an inference server is throughput, which describes how much work the system can process over a given period. For an LLM server, throughput can be measured in terms of tokens processed per second or requests handled over time. Improving throughput can reduce the amount of hardware required to support a particular workload.
vLLM is designed to maximize throughput while making efficient use of GPU memory. It uses techniques such as PagedAttention, continuous batching, quantization, tensor parallelism, and speculative decoding to improve inference performance and resource utilization.
The practical result depends on the workload, model, hardware, and configuration, so benchmark results should always be measured using the workload an application actually expects.
4. vLLM Quickstart
Running vLLM locally requires a system with enough computing resources to load and serve a language model. For this quickstart, we will use an NVIDIA GPU because the standard vLLM installation is designed to work with CUDA. A GPU with compute capability 7.0 or newer is recommended, which includes NVIDIA GPUs such as the V100, T4, A10, A100, H100, and RTX 20, 30, and 40 series.
You should also have CUDA 11.8 or newer, a compatible NVIDIA driver, and a supported Python version. The amount of GPU memory required depends on the model being served. As a general starting point, around 16 GB of VRAM is suitable for many 7B-class models, while larger models require more memory. The actual memory requirement can vary depending on the model, precision, context length, and serving configuration.
For this example, we will use Qwen/Qwen2.5-1.5B-Instruct. Its relatively small size makes it a reasonable model for learning the basic vLLM workflow before moving to larger models.
Installing vLLM with pip
The simplest way to install vLLM is with pip. It is recommended to create a Python virtual environment first so that vLLM and its dependencies remain isolated from other Python projects on your machine.
Create a directory for the example and move into it:
mkdir vllm-quickstart cd vllm-quickstart
Create and activate a virtual environment:
python3 -m venv vllm-env source vllm-env/bin/activate
Once the environment is active, install vLLM:
pip install vllm
After installation, verify that the vLLM command is available:
vllm --help
The CLI provides commands such as serve, chat, complete, and other utilities for working with models and inference servers.
Running vLLM with Docker
Docker provides another way to run vLLM without installing all of its dependencies directly into the host’s Python environment. This can be useful when you want a more isolated and reproducible setup, especially when preparing an environment for deployment.
First, pull the vLLM OpenAI-compatible server image:
docker pull vllm/vllm-openai:latest
You can then start the server and provide access to the NVIDIA GPUs:
docker run --runtime nvidia --gpus all \
-v ~/.cache/huggingface:/root/.cache/huggingface \
-p 8000:8000 \
--ipc=host \
vllm/vllm-openai:latest \
--model Qwen/Qwen2.5-1.5B-Instruct
The --gpus all option makes the available NVIDIA GPUs accessible to the container, while port 8000 exposes the vLLM server to the host machine. The Hugging Face cache is mounted so that downloaded model files can be reused rather than downloaded again whenever a new container is started.
The --model option specifies the model that vLLM should load. In this example, Qwen/Qwen2.5-1.5B-Instruct is loaded when the server starts. Once the model has finished loading, vLLM provides an API endpoint that applications can use to send inference requests.
For a Mac with Apple Silicon, the NVIDIA CUDA setup shown above is not applicable. Apple Silicon uses a different acceleration path, so the Mac-specific vLLM-Metal setup should be used instead.
Starting a vLLM Server
The easiest way to expose a model through vLLM is the vllm serve command.
vllm serve Qwen/Qwen2.5-1.5B-Instruct
When the server starts, vLLM loads the model and exposes an HTTP API. By default, the server listens on port 8000. You can verify that the server is running by requesting the available models:
curl http://localhost:8000/v1/models
The response contains information about the model being served. At this point, the language model is no longer just a file downloaded from a model repository. It is running behind an inference server that applications can communicate with over HTTP.
Sending a Request to the Model
Because vLLM provides an OpenAI-compatible API, you can send a chat completion request using a familiar JSON structure. For example:
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-1.5B-Instruct",
"messages": [
{
"role": "user",
"content": "Explain what an inference server does."
}
],
"temperature": 0.7,
"max_tokens": 200
}'
The server processes the request, runs the model, and returns the generated response as JSON. This OpenAI-compatible interface means that existing applications can often be adapted to communicate with a locally hosted vLLM model by changing the API endpoint and model configuration.
For example, a Python application using the OpenAI client can point its base_url at the local vLLM server:
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="token-abc123"
)
response = client.chat.completions.create(
model="Qwen/Qwen2.5-1.5B-Instruct",
messages=[
{
"role": "user",
"content": "Explain vLLM in simple terms."
}
]
)
print(response.choices[0].message.content)
The API key in this example is only illustrative. For a local development server, authentication may not be necessary, while a production deployment should be protected appropriately. The vLLM documentation also notes that its --api-key option only protects specific API paths, so it should not be treated as the complete security solution for a production deployment.
5. vLLM Compared With Running a Model Directly
It is possible to load a model directly using libraries such as Transformers, so it is reasonable to ask why an inference engine such as vLLM is necessary.
For experimentation, directly loading a model may be perfectly adequate. However, production serving introduces additional requirements. The server needs to handle multiple users, schedule requests, manage GPU memory, provide an API, and maintain predictable performance under changing workloads.
vLLM is designed around those serving requirements. Its inference engine manages the execution of requests while techniques such as continuous batching and PagedAttention improve how available hardware is used. The result is a serving layer designed for workloads where inference efficiency and concurrency matter.
6. When Should You Use vLLM?
vLLM is a good choice when you need to serve an open-source or compatible language model and care about inference throughput, concurrency, GPU utilization, or deployment control.
It makes more sense when an application has a meaningful inference workload rather than simply requiring a model for occasional experimentation. A developer testing a small model locally may not notice much benefit from sophisticated serving optimizations, while a production application handling thousands or millions of requests can benefit considerably from efficient scheduling and memory management.
Self-hosting also gives organizations greater control over their models, hardware, and data. At the same time, vLLM is not automatically the best choice for every situation. A small local experiment may be easier with a simpler runtime, while applications with specific hardware, latency, or model requirements should benchmark their chosen serving solution before making a production decision.
7. Conclusion
vLLM is an open-source inference engine and serving system designed to make LLM inference more efficient. Rather than being another language model, it provides the infrastructure needed to run models and expose them to applications.Its approach combines efficient KV-cache management through PagedAttention with techniques such as continuous batching, quantization, speculative decoding, and tensor parallelism. These capabilities are aimed at increasing throughput, improving GPU utilization, and reducing the resources required to serve large numbers of inference requests.
For developers experimenting with self-hosted LLMs, vLLM provides a practical path from running a model locally to building a production-oriented inference service.
Sources
The article was formulated using the following sources:
- Red Hat — What is vLLM?
- Red Hat — How vLLM accelerates AI inference: 3 enterprise use cases
- vLLM — Quickstart Documentation
- vLLM — OpenAI-Compatible Server
