You have downloaded an open source Large Language Model.

Maybe it is Llama, Qwen, Gemma, or another model.

You load it on an NVIDIA GPU and send a prompt:

1What is the capital of Bangladesh?

The model answers:

1The capital of Bangladesh is Dhaka.

Everything works.

But then 100 users start sending requests at the same time.

Suddenly things become harder.

Some requests wait.

GPU memory fills up.

Your expensive GPU may not be used efficiently.

Response time increases.

This is where vLLM becomes useful.

vLLM is an inference and serving engine designed to run Large Language Models efficiently. It focuses heavily on GPU utilization, memory management, scheduling, and serving many requests at the same time.

In this article, we will understand how vLLM works without going too deep into CUDA or GPU programming.

First, What Exactly Is vLLM?

One important thing to understand is this:

vLLM is not an LLM.

Llama is an LLM. Qwen is an LLM. Gemma is an LLM.

vLLM is the system that runs and serves those models efficiently.

Think about a restaurant.

The LLM is the chef. The GPU is the kitchen. vLLM is the restaurant manager.

The manager decides:

Who gets served first?

Which orders can be prepared together?

How should the kitchen space be used?

When should a new customer enter?

How can we avoid doing the same work twice?

A good chef with a badly managed restaurant can still serve customers slowly.

The same idea applies to GPUs.

You can have a powerful A100 or H100, but poor inference software can still waste much of its potential. vLLM tries to solve that problem.

What Happens Without a Good Serving Engine?

Imagine three users send requests.

 1User A
 2
 3Explain machine learning.
 4
 5
 6User B
 7
 8Write a Python function.
 9
10
11User C
12
13Summarize this document.

These requests can have very different sizes.

User A might generate 100 tokens. User B might generate 500 tokens. User C might send a 10,000 token document and generate 300 tokens.

This creates several problems.

The requests do not start at the same time.

They do not finish at the same time.

They use different amounts of GPU memory.

Their prompts have different lengths.

Their answers have different lengths.

Managing all of this efficiently is difficult.

vLLM contains a scheduler and memory management system designed specifically for this type of workload. Modern vLLM also supports continuous batching, PagedAttention, prefix caching, chunked prefill, optimized GPU execution, quantization, and speculative decoding.

The Big Picture

A simplified vLLM request looks like this:

 1User
 2 ↓
 3API Server
 4 ↓
 5Tokenizer
 6 ↓
 7vLLM Scheduler
 8 ↓
 9KV Cache Manager
10 ↓
11Model Runner
12 ↓
13GPU
14 ↓
15Generated Tokens
16 ↓
17User

There are many details inside each part, but this simplified picture is enough to understand the main idea.

Now let us follow one request through the system.

Step 1: The Request Arrives

Imagine your application sends this request:

1Explain neural networks in simple language.

vLLM can expose an API that follows the OpenAI API protocol, which makes it easier to connect existing applications to a locally hosted or privately hosted model.

Your application sends the text to the vLLM server.

But the model cannot directly understand words.

The text first needs to become tokens.

Step 2: vLLM Converts Text Into Tokens

Suppose we have:

1Machine learning is amazing.

A tokenizer might convert this into something conceptually similar to:

1Machine
2learning
3is
4amazing
5.

Internally these tokens are represented using numbers.

For example:

1[14523, 6975, 374, 8056, 13]

The exact numbers depend on the model.

Now vLLM has something the model can process. But it does not necessarily send the request straight to the GPU.

First, something very important happens.

The scheduler looks at the requests.

Step 3: The Scheduler Decides What the GPU Should Work On

Imagine the GPU is a factory.

Many jobs are waiting outside.

1Request A
2Request B
3Request C
4Request D
5Request E

The factory has limited capacity.

You cannot simply throw everything inside. Someone needs to decide what should run. That is one of the scheduler's jobs.

The vLLM scheduler decides which requests and how many tokens should be processed during the next execution step.

It also works with the KV cache manager to determine whether enough memory is available.

Think of it as a traffic controller.

1                    Scheduler
2                       ↓
3         ┌─────────────┼─────────────┐
4         ↓             ↓             ↓
5     Request A     Request B     Request C
6                       ↓
7                      GPU

The scheduler continuously makes these decisions while requests enter and leave the system.

This is one reason vLLM is useful when many users are accessing the same model.

Step 4: The Model Processes the Prompt

Now imagine the user sends:

1Explain how photosynthesis works.

Before generating an answer, the model needs to process the prompt.

This stage is usually called prefill.

Suppose the prompt contains 2,000 tokens.

The model processes those input tokens and builds information that will be useful when generating the answer.

Some of this information is stored in something called the KV cache.

To understand why vLLM is fast, we need to understand the KV cache.

What Is the KV Cache?

Large Language Models generate text one token at a time.

Imagine the model generates:

1Artificial

Then:

1Artificial intelligence

Then:

1Artificial intelligence is

Then:

1Artificial intelligence is changing

When generating the next token, the model needs information about the tokens that came before it.

Without caching, the model would have to repeat a lot of previous calculations.

That would be wasteful.

Instead, transformer models store useful information about previous tokens.

This information is called the KV cache.

KV means:

Key

and

Value

You do not need to understand the mathematics behind keys and values to understand vLLM.

The important part is this:

The KV cache helps the model avoid repeating attention calculations for tokens it has already processed.

The problem is that the KV cache can consume a large amount of GPU memory, especially when many requests or long sequences are being processed. The original vLLM research identified KV cache memory management as a major limitation for high throughput LLM serving.

And this leads us to one of the most important ideas in vLLM.

PagedAttention

PagedAttention is one of the ideas that made vLLM well known.

To understand it, imagine we have GPU memory like this:

1GPU Memory
2
3████████████████████████████████

Every request needs some space for its KV cache.

The problem is that we do not know exactly how much space each request will eventually need.

A user might generate 50 tokens.

Another might generate 500.

Another might generate 5,000.

Traditional memory allocation can therefore waste space.

Imagine a parking lot where every car receives a parking area large enough for a bus.

 1Car A
 2
 3[             ]
 4
 5Car B
 6
 7[             ]
 8
 9Car C
10
11[             ]

Most of the space is empty.

That is inefficient.

PagedAttention takes a different approach.

Instead of treating each request as needing one large continuous area of memory, vLLM divides KV cache memory into smaller blocks. Those blocks can be allocated when they are needed.

Conceptually:

 1GPU KV Cache
 2
 3[Block 1]
 4[Block 2]
 5[Block 3]
 6[Block 4]
 7[Block 5]
 8[Block 6]
 9[Block 7]
10[Block 8]

Request A might use:

1Block 1
2Block 4
3Block 7

Request B might use:

1Block 2
2Block 3

The blocks do not need to sit next to each other in physical memory.

This is similar to the idea of paging in operating systems.

That is where the name PagedAttention comes from.

The result is better memory usage.

Better memory usage means vLLM can often fit more active requests into the same GPU memory.

And that can increase throughput.

Why Memory Efficiency Matters So Much

Suppose your GPU has enough memory for:

1Model weights
2
3plus
4
510 active requests

If memory is managed more efficiently, maybe you can support:

1Model weights
2
3plus
4
520 active requests

The exact numbers depend on the model and workload.

But the general idea is simple.

More efficient memory management allows more useful work to happen on the same hardware.

The original vLLM paper reported significant throughput improvements compared with the serving systems tested by the researchers, especially for longer sequences and larger models.

Step 5: Continuous Batching Keeps the GPU Busy

Now we reach another major idea.

Continuous batching.

Imagine four requests enter the server.

1Request A
2Request B
3Request C
4Request D

Traditional batching might put them together.

1Batch
2
3A
4B
5C
6D

But suppose Request A finishes quickly.

Request B is still running.

Request C is still running.

Request D is still running.

In a simple batching system, the empty space created by Request A may not immediately be used by another request.

That is wasteful.

Continuous batching works differently.

When Request A finishes, another waiting request can enter.

For example:

1Before
2
3A
4B
5C
6D

A finishes.

Then:

1E
2B
3C
4D

Later C finishes.

Then:

1E
2B
3F
4D

Requests continuously enter and leave the active group.

vLLM lists continuous batching as one of its main serving optimizations.

Think again about our restaurant.

A normal batching system might say:

We will wait until everyone at every table finishes before seating new customers.

Continuous batching says:

A table is free. Seat the next customer immediately.

This helps keep the GPU busy.

Step 6: The Model Generates Tokens

After the prefill stage, generation begins.

Suppose the answer is:

1Photosynthesis allows plants to convert light into energy.

The model generates something conceptually like this:

1Photosynthesis

Then:

1allows

Then:

1plants

Then:

1to

Then:

1convert

And so on.

This stage is commonly called decode.

After every generation step, vLLM updates the KV cache.

The scheduler then decides what work should happen during the next step.

The process repeats.

 1Schedule requests
 2      ↓
 3Run model
 4      ↓
 5Generate tokens
 6      ↓
 7Update KV cache
 8      ↓
 9Schedule again
10      ↓
11Run model again
12      ↓
13Generate more tokens
14      ↓
15...

This loop continues until each request finishes.

Step 7: Tokens Can Be Streamed Back to the User

Users usually do not want to wait for the entire answer.

Instead, applications often stream tokens.

The user sees:

1Photosynthesis

Then:

1Photosynthesis allows

Then:

1Photosynthesis allows plants

And the answer continues appearing.

This creates the familiar ChatGPT style experience where text appears gradually.

The model may still be generating the rest of the response while the user is already reading the beginning.

So What Is vLLM Really Doing?

At this point, we can simplify the entire system.

vLLM is mainly trying to answer three questions again and again.

Question 1

Which requests should the GPU process right now?

The scheduler helps answer this.

Question 2

Where should the KV cache for those requests live?

PagedAttention and the KV cache manager help answer this.

Question 3

How can we keep the GPU doing useful work?

Continuous batching and optimized model execution help answer this.

That is the heart of vLLM.

There are many advanced features around these ideas, but if you understand these three questions, you understand much of the reason vLLM exists.

Prefix Caching

vLLM can also reuse KV cache information when requests share the same prefix. In current vLLM V1, automatic prefix caching is managed through the KV cache manager.

Imagine an AI agent where every request starts with this:

1You are a customer support assistant.
2
3Follow these instructions.
4
5You have access to these 30 tools.
6
7Here are the tool definitions.
8
9Here are 20 examples.

Suppose this is 8,000 tokens.

Then User A asks:

1Where is my order?

User B asks:

1Cancel my subscription.

User C asks:

1Change my email address.

The first 8,000 tokens may be identical.

Without caching:

1User A → Process 8,000 tokens
2
3User B → Process 8,000 tokens
4
5User C → Process 8,000 tokens

With prefix caching, vLLM can reuse KV cache blocks from previously processed prefixes when they match.

Conceptually:

1Common 8,000 token prefix
2          ↓
3      Cached once
4          ↓
5    ┌─────┼─────┐
6    ↓     ↓     ↓
7 User A User B User C

For applications with large repeated system prompts, this can save a lot of repeated work.

Chunked Prefill

Another useful feature is chunked prefill.

Imagine one request contains a huge prompt:

130,000 input tokens

Processing that entire prompt can require a lot of computation.

Meanwhile, other users may already be waiting for their next generated token.

Chunked prefill allows vLLM to split large prefill work into smaller pieces and schedule that work alongside decode requests.

Instead of thinking:

1Process all 30,000 tokens first.

Think:

1Process part of the long prompt.
2
3Generate tokens for existing requests.
4
5Process another part.
6
7Generate more tokens.
8
9Continue.

This can help balance throughput and interactive response latency.

What Happens When GPU Memory Becomes Full?

Imagine the GPU KV cache is almost full.

But new requests continue arriving.

vLLM cannot create unlimited GPU memory.

When KV cache space becomes insufficient, vLLM can preempt some requests so that memory becomes available for other work. Those requests can later be recomputed when capacity becomes available.

This is another reason the scheduler and KV cache manager are closely connected.

The scheduler cannot only think about computation.

It also needs to think about memory.

vLLM Does Not Only Use PagedAttention

People sometimes explain vLLM like this:

1vLLM = PagedAttention

That is too simple.

PagedAttention is important, but modern vLLM contains many other optimizations.

These include continuous batching, chunked prefill, prefix caching, optimized GPU kernels, CUDA graph support, quantization, speculative decoding, and different forms of parallel execution.

So a better mental model is:

 1                 vLLM
 2                   │
 3     ┌─────────────┼─────────────┐
 4     │             │             │
 5 Scheduler    Memory System   GPU Execution
 6     │             │             │
 7 Continuous    KV Cache      Optimized
 8 Batching      Management    Kernels
 9                   │
10             PagedAttention
11                   │
12             Prefix Caching

All these pieces work together.

An Example With Multiple Users

Imagine we are serving a model on an A100.

Five users arrive.

1User A → 2,000 token prompt
2
3User B → 500 token prompt
4
5User C → 7,000 token prompt
6
7User D → 1,000 token prompt
8
9User E → waiting

vLLM's scheduler chooses work from the active requests.

The KV cache manager allocates blocks for their KV caches.

PagedAttention allows those caches to use GPU memory in blocks.

The GPU processes tokens for multiple requests.

Then User B finishes.

Its KV cache blocks can eventually become available again.

User E can enter the active workload.

Meanwhile, User C still has a large prompt, so chunked prefill may allow its prompt processing to be mixed with generation work from other requests.

Conceptually:

 1Time 1
 2
 3A B C D
 4
 5
 6Time 2
 7
 8A B C D
 9
10
11Time 3
12
13B finishes
14
15
16Time 4
17
18A E C D
19
20
21Time 5
22
23D finishes
24
25
26Time 6
27
28A E C F

This constant movement is why continuous batching is so useful.

The server does not treat inference as one fixed batch.

It treats inference as a continuously changing workload.

Why Is vLLM Faster Than a Simple Model Server?

There is an important distinction here. vLLM does not magically make the neural network smaller. It does not make Llama suddenly require half as many transformer layers.

Instead, vLLM tries to reduce waste around running the model.

Think about a supermarket.

Imagine ten checkout counters.

A poorly managed supermarket might have:

1Counter 1 → busy
2
3Counter 2 → empty
4
5Counter 3 → empty
6
7Counter 4 → huge queue
8
9Counter 5 → empty

The supermarket owns enough hardware.

The problem is scheduling.

vLLM tries to manage expensive GPU resources more intelligently.

The improvement comes from things such as:

Better KV cache memory usage.

More requests running together.

Less wasted GPU capacity.

Better scheduling.

Reuse of cached computation.

Optimized GPU execution.

Throughput and Latency Are Different

When discussing vLLM, it is useful to understand two different goals.

Latency

Latency means:

How long does one user wait?

For example:

1Request sent
2     ↓
31.2 seconds
4     ↓
5First token appears

Throughput

Throughput means:

How much total work can the system process?

For example:

15,000 generated tokens every second

or:

1100 requests every second

vLLM is especially designed around efficient, high throughput serving while still supporting interactive workloads.

Sometimes increasing throughput can hurt individual request latency.

That is why settings such as batch size, token budget, KV cache size, and parallelism should be tuned for your actual workload.

A Simple Mental Model

If you remember only one thing from this article, remember this analogy.

Imagine your GPU is a hotel.

The LLM weights occupy a large part of the hotel permanently.

Requests are guests.

The KV cache is the room space each guest needs.

The scheduler is the hotel manager.

PagedAttention divides available space into smaller manageable units.

Continuous batching means new guests can enter whenever capacity becomes available.

Prefix caching allows useful shared information to be reused.

Chunked prefill prevents one enormous guest from taking over too much service capacity at once.

vLLM coordinates all of this.

Without good management, an expensive hotel can still serve very few guests.

With good management, the same hotel can serve many more.

The Full Request Flow

Now we can put everything together.

 1Application
 2     ↓
 3Request arrives
 4     ↓
 5Text becomes tokens
 6     ↓
 7Scheduler receives request
 8     ↓
 9KV cache blocks are allocated
10     ↓
11Prompt is processed
12     ↓
13KV cache is stored
14     ↓
15Model generates next token
16     ↓
17KV cache is updated
18     ↓
19Scheduler creates the next group of work
20     ↓
21More requests enter through continuous batching
22     ↓
23GPU runs the next model execution
24     ↓
25More tokens are generated
26     ↓
27Tokens are streamed back
28     ↓
29Request finishes
30     ↓
31KV cache blocks become reusable

This happens continuously for many requests.

Where PagedAttention Fits

If you see diagrams of vLLM, PagedAttention can sometimes appear to be the entire system.

It is better to think about it like this:

1                    vLLM
2                      ↓
3                  Scheduler
4                      ↓
5                KV Cache Manager
6                      ↓
7                PagedAttention
8                      ↓
9                 GPU Memory

PagedAttention is mainly about making attention and KV cache memory management work efficiently with paged blocks.

The scheduler is responsible for deciding what should run.

The model runner performs the model computation.

Continuous batching keeps changing the active group of requests.

Together, these parts create the serving system.

What About Speculative Decoding?

vLLM also supports speculative decoding. The current documentation describes it as a technique that can reduce the time between generated tokens for certain workloads, especially some workloads with lower request volume where inference is limited by memory movement.

The idea is simple.

Normally:

1Large model
2
3Token 1
4 ↓
5Token 2
6 ↓
7Token 3
8 ↓
9Token 4

With speculative decoding, another mechanism predicts several possible tokens.

 1Draft
 2
 3Token 1
 4Token 2
 5Token 3
 6Token 4
 7
 8       ↓
 9
10Large model checks them

If several predictions are correct, generation can move forward faster.

This is an additional optimization.

It is not the basic reason vLLM works.

What vLLM Does When You Start the Server

At startup, vLLM has several important jobs.

It loads the model.

It loads the tokenizer.

It prepares GPU memory.

It determines KV cache capacity.

It creates the serving engine.

It prepares the model runner.

Then it waits for requests.

Once traffic arrives, the repeating process begins:

 1Receive
 2   ↓
 3Schedule
 4   ↓
 5Allocate memory
 6   ↓
 7Run model
 8   ↓
 9Generate
10   ↓
11Update cache
12   ↓
13Schedule again

This loop may happen many times every second.

The Main Idea Behind vLLM

The easiest way to understand vLLM is not to think about one clever algorithm.

Think about resource management.

LLM serving has three expensive resources:

1GPU Compute
2
3GPU Memory
4
5Time

vLLM tries to use all three efficiently.

PagedAttention helps with memory.

Continuous batching helps with GPU utilization.

The scheduler coordinates requests.

The KV cache avoids unnecessary repeated attention calculations.

Prefix caching can avoid repeated work across requests.

Chunked prefill helps schedule long prompts alongside generation.

Optimized kernels and GPU execution help perform the actual computations efficiently.

That combination is what makes vLLM useful.

Final Takeaway

You can summarize vLLM in one sentence:

vLLM is a serving engine that tries to keep GPUs busy while using GPU memory intelligently.

The most important concepts to remember are:

Scheduler: decides what work happens next.

KV cache: stores useful information from previously processed tokens.

PagedAttention: manages KV cache memory using smaller blocks.

Continuous batching: continuously adds and removes requests from active GPU work.

Prefix caching: reuses computation when requests share the same beginning.

Chunked prefill: splits large prompt processing into smaller pieces.

Once you understand those ideas, vLLM becomes much easier to understand.

The model itself is still doing the same fundamental job:

1Input tokens
2     ↓
3Transformer
4     ↓
5Predict next token

vLLM's job is to make sure that this process happens efficiently when many real users are asking the model for answers at the same time.

That is the difference between simply running an LLM and building an efficient LLM serving system.