How vLLM Supercharges LLM Inference: Faster, Cheaper, Scalable AI Serving

large_language_model

How vLLM Supercharges LLM Inference: Faster, Cheaper, Scalable AI Serving

Discover how vLLM transforms LLM performance with paged memory, batching, and quantization for real-world scalability.

Dec 30, 2025

How vLLM Supercharges LLM Inference: Faster, Cheaper, Scalable AI Serving

Let’s talk about something I’ve been obsessed with lately: speed. Not in a reckless way, but in the practical, everyday reality of trying to get large language models to actually work for people. If you’ve ever tried to serve an open-source model to more than one user at a time, you know the feeling. The delay, the high costs, the frustrating feeling that the powerful GPU sitting in your server is mostly just… waiting.

This is what pushed me to look beyond the basics. I was tired of theoretical benchmarks and wanted to build something that could handle real, unpredictable traffic. That search led me down a rabbit hole of optimization and, eventually, to a tool that changed my perspective: vLLM. This isn’t just another library; it’s a fundamental rethinking of how models use memory during inference. I want to show you how it works and how you can use it to build pipelines that are fast, efficient, and ready for production. By the end of this, you’ll see your GPU not as a bottleneck, but as a powerhouse you’re finally using properly.

The core problem vLLM solves is one you’ve likely felt but maybe couldn’t name: memory fragmentation. Think about how a traditional system runs. To generate text, the model needs to keep a “key-value cache” of all the previous tokens in a conversation. Old systems would pre-allocate a giant, contiguous block of memory for this cache for every single user request, guessing the maximum length of the conversation. It’s like giving every customer who walks into a cafe a massive, empty banquet hall just in case they bring 50 friends. Most sit alone, and all that space is wasted, preventing new customers from coming in.

vLLM applies a classic computer science idea to this new problem: paged memory. Instead of one big hall per customer, it has a warehouse of small, fixed-size “blocks” or tables. When you start a conversation, you get one table. As your conversation grows longer and needs more space, the system simply adds another table nearby. Your “conversation” is now seated across several small tables that aren’t necessarily right next to each other, but a map (the “block table”) keeps track of the order. This means memory is packed tightly. No wasted space. Suddenly, the cafe can serve many, many more customers at once.

How much of a difference can this simple idea really make? The numbers speak for themselves. We’re often talking about a 10x to 20x increase in the number of users you can serve simultaneously compared to older systems. That’s not a small tweak; it’s a revolution in throughput.

Getting started is straightforward. First, let’s set up a clean environment. I always recommend a virtual environment to avoid dependency hell.

python -m venv vllm_env
source vllm_env/bin/activate  # On Windows: vllm_env\Scripts\activate
pip install vllm

That’s it for the core engine. Now, you need a model. Let’s grab a capable, popular one like Mistral 7B.

# A simple script to fetch your model
from huggingface_hub import snapshot_download

model_path = snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.2")
print(f"Model downloaded to: {model_path}")

With the model ready, let’s build the simplest possible engine. This is where you’ll see the immediate benefit.

from vllm import LLM, SamplingParams

# Initialize the engine. Notice the `gpu_memory_utilization`.
# This tells vLLM how much of your GPU's memory it can use.
llm_engine = LLM(model="mistralai/Mistral-7B-Instruct-v0.2",
                 gpu_memory_utilization=0.9,
                 trust_remote_code=True)

# Define how you want text generated.
sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=150)

# Now, generate responses for a batch of prompts at once.
prompts = [
    "What is the capital of France?",
    "Explain the concept of gravity to a five-year-old.",
    "Write a haiku about programming."
]

outputs = llm_engine.generate(prompts, sampling_params)

for output in outputs:
    generated_text = output.outputs[0].text
    print(f"Prompt: {output.prompt[:50]}...")
    print(f"Answer: {generated_text}\n---")

Did you notice what happened there? We fed it three completely different questions in one go. The system didn’t process them one after another. It batched them. This is the second magic trick: continuous batching. In a live server, requests are coming in at random times. Old systems would wait to group them, causing delays. vLLM’s scheduler continuously groups and regroups these requests on the fly. When one sequence in the batch finishes generating, its slot is instantly filled by a new waiting request. The GPU never stops working. It feels like the model is giving each user its full, dedicated attention, while under the hood it’s efficiently serving a crowd.

But a script isn’t a service. For a real application, you need an API. This is where you can start to feel the power. Let’s create a robust, asynchronous server using FastAPI. This will handle multiple concurrent requests smoothly.

from fastapi import FastAPI, BackgroundTasks
from pydantic import BaseModel
from vllm import SamplingParams
import asyncio
from typing import List

# Use the engine we created globally for efficiency.
# In production, you'd manage this lifecycle more carefully.
app = FastAPI()
llm_engine = LLM(model="mistralai/Mistral-7B-Instruct-v0.2")

class CompletionRequest(BaseModel):
    prompt: str
    max_tokens: int = 100

@app.post("/generate")
async def generate_text(request: CompletionRequest):
    """A simple, non-streaming endpoint."""
    sampling_params = SamplingParams(max_tokens=request.max_tokens)
    result = llm_engine.generate([request.prompt], sampling_params)
    return {"response": result[0].outputs[0].text}

That’s functional, but what about streaming word by word, like ChatGPT? Users expect that now. It reduces perceived latency. vLLM supports this natively.

from sse_starlette.sse import EventSourceResponse
import json

@app.get("/stream")
async def stream_text(prompt: str, max_tokens: int = 200):
    """Stream tokens as they are generated."""
    sampling_params = SamplingParams(max_tokens=max_tokens)
    
    async def event_generator():
        # This generator yields tokens from the vLLM engine.
        for output in llm_engine.generate([prompt], sampling_params, use_tqdm=False):
            # The output is yielded progressively
            for token in output.outputs[0].token_ids:
                # In reality, you'd decode token IDs to text here.
                # This is simplified for the example.
                yield {"data": json.dumps({"token": token})}
    
    return EventSourceResponse(event_generator())

Now you have a server that can batch multiple users and stream to each individually. But what if your model is too big for one GPU? Or what if you want to serve it to thousands of users? This is where we step into more advanced territory.

First, let’s talk about making the model itself smaller and faster through quantization. It’s a way to reduce the numerical precision of the model’s weights, trading a tiny, often negligible, amount of accuracy for massive gains in speed and memory savings. vLLM supports several methods.

# Initializing with AWQ quantization (a popular, accurate method)
llm_engine_quantized = LLM(
    model="TheBloke/Mistral-7B-Instruct-AWQ",
    quantization="awq",
    gpu_memory_utilization=0.9
)

With quantization, you might fit a 13B parameter model in the same space a 7B model used to take. What if you have more than one GPU? You can split the model across them using tensor parallelism. It’s as simple as changing one parameter.

# This will spread the model across 2 GPUs.
llm_engine_parallel = LLM(
    model="mistralai/Mistral-7B-Instruct-v0.2",
    tensor_parallel_size=2,
    gpu_memory_utilization=0.85  # Slightly less per GPU for communication overhead
)

The true test, though, is in a production deployment. You need metrics, health checks, and resilience. Here’s a minimal look at how you might add monitoring.

from prometheus_client import Counter, Histogram, generate_latest
from fastapi import Response

REQUEST_COUNTER = Counter('llm_requests_total', 'Total requests served')
GENERATION_TIME = Histogram('llm_generation_seconds', 'Time spent generating')

@app.post("/generate_measured")
async def generate_measured(request: CompletionRequest):
    REQUEST_COUNTER.inc()
    with GENERATION_TIME.time():
        result = llm_engine.generate([request.prompt], SamplingParams(max_tokens=request.max_tokens))
    return {"response": result[0].outputs[0].text}

@app.get("/metrics")
async def metrics():
    return Response(generate_latest())

So, where does this leave us? You start with a concept—paged attention—that fixes a broken memory system. You layer on continuous batching to keep the hardware saturated. You wrap it in a modern async API. Then you optimize with quantization and parallelism for scale. What you build is no longer a fragile prototype; it’s a robust pipeline.

The journey from a slow, expensive model to a fast, scalable service is one of the most satisfying technical challenges I’ve worked on. It turns a research artifact into a practical tool. I’ve shared the steps that worked for me, from the core concepts to the code snippets you can run today.

Have you run into specific bottlenecks while trying to serve LLMs? What tricks have you found to squeeze out more performance? I’d love to hear about your experiences in the comments below. If this guide helped you see your GPU in a new light, please share it with someone else who might be stuck in the waiting phase. Let’s build faster, more efficient systems together.

As a best-selling author, I invite you to explore my books on Amazon. Don’t forget to follow me on Medium and show your support. Thank you! Your support means the world!

101 Books

101 Books is an AI-driven publishing company co-founded by author Aarav Joshi. By leveraging advanced AI technology, we keep our publishing costs incredibly low—some books are priced as low as $4—making quality knowledge accessible to everyone.

Check out our book Golang Clean Code available on Amazon.

Stay tuned for updates and exciting news. When shopping for books, search for Aarav Joshi to find more of our titles. Use the provided link to enjoy special discounts!

📘 Checkout my latest ebook for free on my channel!
Be sure to like, share, comment, and subscribe to the channel!

Our Creations

Be sure to check out our creations:

We are on Medium

Share: Facebook Twitter Reddit LinkedIn WhatsApp Telegram Pinterest Email Instagram

large_language_model