How to use vLLM for LLM inferencing?
Running LLMs is expensive. Not just in money, but in GPU memory, latency, and throughput. Every time you chat with an AI model, it’s generating tokens one by one, keeping track of the full conversation history inside GPU memory. And that memory management turns out to be a nightmare when you try to scale to thousands of users.
That’s where vLLM comes in. It’s not another model, it’s a runtime engine that makes existing models run faster and more efficiently. Think of it as the Turbocharger for models like Llama, Mistral, or Falcon.
Why do we even need something like vLLM?
Let’s start with a simple example.
Suppose you’re hosting a model like Llama-3–8B on your GPU server. You receive multiple requests at once:
- One user wants a 20-token response.
- Another wants a 2,000-token essay.
- A third user cancels halfway.
In a standard setup, each request gets a fixed chunk of GPU memory. The problem? Once a short request ends, that memory can’t be reused immediately. The GPU ends up fragmented, some memory slots sit idle while others overflow. This wastes VRAM and slows everything down.
When you multiply this by hundreds of concurrent requests, the server either crashes or sits half-idle.
This inefficiency has been a core bottleneck in LLM deployment. The model itself is fast enough; it’s the memory management and batching that ruin it.
Enter vLLM
vLLM (developed by researchers at UC Berkeley) was designed to solve exactly this.
The magic lies in a feature called PagedAttention, a new way of handling memory allocation for the model’s Key-Value cache (KV cache).
The KV Cache Problem (in simple words)
When a model generates tokens, it stores “attention history” , basically, it remembers which tokens came before. This memory grows as the conversation goes on.In traditional systems, this memory is stored as one big block per request. When you finish, it’s discarded. No reuse, no flexibility.
What PagedAttention Does
PagedAttention breaks that big chunk into small pages (like virtual memory pages in your computer). Instead of reserving a huge continuous block for each request, vLLM allocates these small pages on demand, moves them around efficiently, and reuses them across requests.
Result:
- Less fragmentation: GPU memory gets used optimally.
- Longer context windows: You can now serve models with 32k+ context efficiently.
- Higher throughput: More concurrent users without extra GPUs.
What vLLM Actually Does
Under the hood, vLLM manages:
- PagedAttention for efficient KV cache management.
- Continuous batching, which merges incoming requests dynamically, so you don’t need to wait for a full batch to start.
- Tensor parallelism, for splitting large models across multiple GPUs.
- OpenAI-compatible API: so you can swap out openai SDK calls for your own models served by vLLM.
Basically, it makes inference efficient without you needing to rewrite your codebase.
How to use?
- Pip install vllm
pip install vllm
2. Host the desired LLM
python -m vllm.entrypoints.openai.api_server
--model meta-llama/Llama-3-8B-Instruct
3. Use it as API, simple !!
from openai import OpenAI
client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
response = client.chat.completions.create(
model="meta-llama/Llama-3-8B-Instruct",
messages=[{"role": "user", "content": "Explain what vLLM is in simple terms"}])
print(response.choices[0].message.content)
Why it Matters
The biggest cost in LLM deployment isn’t the model, it’s how efficiently you can serve it. Most GPU clusters sit underutilized because of memory fragmentation and static batching.
vLLM flips that equation:
- You can serve more users on the same GPU.
- You can extend context length without doubling VRAM.
- You get predictable latency, even under mixed workloads.
That’s why companies like Hugging Face, Anyscale, and AWS are now using vLLM as the backend for their LLM inference infrastructure.
Limitations
No system is perfect, and vLLM has its own trade-offs:
- It’s inference-only, no support for fine-tuning or training.
- Multi-node distributed inference is still limited (mostly designed for single-node, multi-GPU).
- PagedAttention can add slight overhead for small models, the real gains appear when you’re serving larger ones.
- Not all quantized model formats are supported out of the box (though support is improving).
Still, for production-grade serving, it’s one of the cleanest and most robust options available.
The Broader Impact
vLLM is quietly changing how open-source models are deployed. Instead of relying on heavyweight frameworks or expensive proprietary inference APIs, you can run your own stack efficiently.
It’s the same shift we saw years ago when Docker made deployment portable, vLLM is doing that for LLM inference. You can bring your model, run it locally, scale it in the cloud, or even serve it through OpenAI-style endpoints all without rewriting anything.
Final Thoughts
vLLM isn’t glamorous in the way new models are, but it’s far more important for anyone actually trying to run these models at scale.
It abstracts away the messy details of GPU memory management, batching, and scheduling letting you focus on what matters: building products.
If you’re serious about deploying open-source LLMs, vLLM is no longer optional. It’s the new baseline.
Wish to host local LLMs as APIs? Use vLLM was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
