Zero to Hero with vLLM: A Practical Guide for High‑Throughput LLM Inference

Introduction

If you’re trying to serve large language models (LLMs) efficiently on GPUs, you quickly run into a wall:

GPU memory gets eaten by KV cache
Throughput collapses as concurrent users increase
You spend more on hardware than on your actual application

vLLM is an open-source inference engine designed to fix this. It combines:

A highly optimized attention implementation (PagedAttention)
Continuous batching and scheduling
A production-ready API server (OpenAI-compatible)
Tight GPU memory management

This tutorial is a concise zero-to-hero guide for developers who want to:

Understand what vLLM is and why it matters
Grasp PagedAttention in simple, intuitive terms
Install vLLM and run a first model-serving example
Use vLLM for high-throughput inference in practice
Avoid common pitfalls and apply best practices
Go further with high-quality learning resources and links

1. What Is vLLM and Why Does It Matter?

1.1 What is vLLM?

vLLM is a fast, production-grade inference engine for LLMs, created by researchers at UC Berkeley and widely used in industry. It focuses exclusively on inference, not training.

Key characteristics:

Optimized GPU memory usage via PagedAttention
High throughput with dynamic/continuous batching
Drop-in server with OpenAI-compatible API
Supports many models from Hugging Face (LLaMA, Mistral, Gemma, etc.)
Multi-GPU support with tensor parallelism
Features like:
- Streaming responses
- LoRA / PEFT adapters
- Speculative decoding (for faster generation, where supported)
- Multi-turn conversation support

You can think of vLLM as the engine behind your LLM service: it doesn’t define the model architecture, it makes running that model fast and efficient.

1.2 Why does vLLM matter?

Serving LLMs at scale is mostly a systems and memory management problem:

Each token produced requires storing key/value (KV) tensors for every layer
Long contexts and many concurrent users blow up KV cache size
Naive implementations either:
- Waste VRAM (overallocating)
- Or repeatedly copy/move/recompute KV data (slow)

vLLM fixes this by:

Handling KV cache like a virtual memory system
Efficiently packing many requests on the same GPU
Keeping throughput high as you scale concurrency

For production scenarios—chatbots, code assistants, RAG systems, multi-tenant APIs—this typically means:

2–4× higher throughput vs naive inference stacks
Much better GPU utilization
Lower cost per token

2. PagedAttention in Simple Terms

PagedAttention is the core innovation behind vLLM. Let’s break it down without heavy math.

2.1 How KV cache usually works

In transformer-based LLMs, attention layers store key (K) and value (V) tensors for all previous tokens so that each new token can attend to the entire history.

In a naive implementation:

For each request (user), you allocate a big contiguous KV buffer
As the sequence grows, the buffer grows
For many concurrent users, you end up with:
- Fragmented VRAM
- Wasted memory (over-provisioning)
- Frequent reallocations or copies

This is similar to allocating a huge continuous array per user.

2.2 The analogy: from big arrays to paged memory

Operating systems solved this problem decades ago with virtual memory and pages:

Memory is split into equal-sized pages
Each process has a page table mapping virtual pages to physical pages
You can:
- Compact, swap, reuse pages
- Efficiently serve many processes using the same physical memory

PagedAttention applies the same idea to KV cache.

2.3 How PagedAttention works (intuitively)

In PagedAttention:

The KV cache is split into fixed-size blocks (pages)
Each sequence (request) has a logical view of its KV cache as a sequence of pages
Instead of storing KV per-sequence contiguously in memory, vLLM:
- Allocates KV pages from a shared pool
- Tracks which pages belong to which sequence
- Allows pages to be reused when a sequence finishes

You get:

Compact packing of many sequences into VRAM
Minimal fragmentation
Efficient reuse when requests complete

From the model’s perspective, nothing changes: it still sees a full history. The engine just maps logical positions to physical pages behind the scenes.

2.4 Why PagedAttention is fast

PagedAttention is fast because:

It allows continuous batching:
- New requests can join a running batch
- Finished requests free pages for new ones
It avoids large-scale memcpy and reshaping of KV tensors
It reduces wasted KV space (you don’t have to reserve worst-case size per request)

In practice, that translates directly into:

Higher throughput (more tokens/sec)
Better latency under load (more concurrent users)
More predictable GPU memory usage

3. Installing vLLM

vLLM supports Linux with NVIDIA GPUs (CUDA). It can work on CPU, but the main benefits are on GPU.

3.1 Prerequisites

You’ll generally need:

OS: Linux (Ubuntu 20.04+ is common)
GPU: NVIDIA with recent drivers
CUDA: 11.8+ (check vLLM README for current versions)
Python: 3.9–3.11 (commonly)
pip or conda

Check GPU and CUDA:

nvidia-smi

3.2 Basic installation via pip

The simplest install:

pip install vllm

For many setups, that’s enough. If you hit GPU/cuda/torch issues, you might instead:

Install PyTorch with the right CUDA version:
```
pip install torch --index-url https://download.pytorch.org/whl/cu121
```
(Adjust cu121 to your CUDA version as needed.)
Then install vLLM:
```
pip install vllm
```

To verify:

python -c "import vllm; print(vllm.__version__)"

If this runs without errors, you’re good.

4. Your First vLLM Example (Python API)

Let’s start by generating text from a model locally using vLLM’s Python API.

4.1 Minimal generation example

Create simple_vllm.py:

from vllm import LLM, SamplingParams

# 1. Choose a model from Hugging Face Hub
model_name = "mistralai/Mistral-7B-Instruct-v0.2"

# 2. Configure sampling
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=128,
)

# 3. Instantiate the LLM engine
#    This will download weights the first time.
llm = LLM(
    model=model_name,
    dtype="bfloat16",     # or "float16" depending on GPU
    trust_remote_code=False,
)

# 4. Define prompts
prompts = [
    "Explain vLLM in one paragraph.",
    "List three practical use cases for vLLM in production.",
]

# 5. Run generation
outputs = llm.generate(prompts, sampling_params)

# 6. Print results
for i, output in enumerate(outputs):
    print(f"=== Prompt {i} ===")
    print("Prompt:", prompts[i])
    print("Output:", output.outputs[0].text.strip())
    print()

Run it:

python simple_vllm.py

On first run, vLLM will:

Download the model from Hugging Face
Load it into GPU memory
Compile kernels/cache some configs

Then generate output for your prompts.

4.2 Notes on this example

LLM is the high-level vLLM engine
SamplingParams configures generation behavior
Passing a list of prompts automatically enables batching
vLLM will internally use PagedAttention + scheduling to efficiently run them

5. Serving Models with vLLM’s OpenAI-Compatible API

Often you don’t want to embed vLLM directly into your app—you want a service. vLLM comes with a built-in server that exposes an OpenAI-compatible API, so you can use your existing OpenAI SDKs and tools.

5.1 Start the API server

A simple server command:

python -m vllm.entrypoints.openai.api_server \
  --model mistralai/Mistral-7B-Instruct-v0.2 \
  --host 0.0.0.0 \
  --port 8000

Common optional flags:

--dtype bfloat16 or --dtype float16
--gpu-memory-utilization 0.9 (fraction of GPU memory vLLM can use)
--max-model-len 4096 or higher (max context length)
--tensor-parallel-size 2 (if using 2 GPUs for sharding)

Example with some tuning:

python -m vllm.entrypoints.openai.api_server \
  --model mistralai/Mistral-7B-Instruct-v0.2 \
  --dtype bfloat16 \
  --gpu-memory-utilization 0.90 \
  --max-model-len 8192 \
  --host 0.0.0.0 \
  --port 8000

5.2 Call the server using curl (Chat Completions)

vLLM’s server exposes routes like /v1/chat/completions and /v1/completions.

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistralai/Mistral-7B-Instruct-v0.2",
    "messages": [
      {"role": "system", "content": "You are a concise assistant."},
      {"role": "user", "content": "Explain what vLLM is in 3 bullet points."}
    ],
    "max_tokens": 128,
    "temperature": 0.7,
    "stream": false
  }'

5.3 Call the server with the OpenAI Python SDK

Because it’s OpenAI-compatible, you can use the same client libraries.

OpenAI Python client v1.x style:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY"  # vLLM does not enforce auth by default
)

resp = client.chat.completions.create(
    model="mistralai/Mistral-7B-Instruct-v0.2",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Give me 3 reasons to use vLLM."},
    ],
    max_tokens=128,
)

print(resp.choices[0].message.content)

You can also enable streaming:

stream = client.chat.completions.create(
    model="mistralai/Mistral-7B-Instruct-v0.2",
    messages=[{"role": "user", "content": "Summarize vLLM in 5 sentences."}],
    max_tokens=128,
    stream=True
)

for chunk in stream:
    content = chunk.choices[0].delta.content or ""
    print(content, end="", flush=True)
print()

6. Using vLLM for High-Throughput Inference

The real power of vLLM appears when you have many users or heavy load. This section covers practical levers you can use.

6.1 Core ideas for high throughput

vLLM’s engine gives you:

Continuous batching: new requests dynamically join existing batches
PagedAttention: efficient KV management under many concurrent requests
Async scheduling: maximize GPU utilization

To benefit from this:

Send many concurrent requests instead of serial calls
Prefer smaller chunks of work per request (reasonable max_tokens)
Avoid extremely long contexts unless necessary

6.2 Tuning important server parameters

Common flags for the api_server:

python -m vllm.entrypoints.openai.api_server \
  --model mistralai/Mistral-7B-Instruct-v0.2 \
  --dtype bfloat16 \
  --gpu-memory-utilization 0.92 \
  --max-model-len 8192 \
  --max-num-seqs 2048 \
  --host 0.0.0.0 \
  --port 8000

Key flags:

--gpu-memory-utilization
- Fraction of GPU memory vLLM can use for model + KV cache
- Typical values: 0.85–0.95
- Too low: leaves unused VRAM, reducing batch size
- Too high: risk of OOM
--max-model-len
- Maximum context length (prompt + generated tokens)
- Larger context ⇒ more KV memory per request ⇒ fewer concurrent sequences
- Tune based on your use case (chat vs long-doc RAG)
--max-num-seqs
- Maximum number of sequences that can be in-flight at once
- Higher ⇒ potentially more throughput, but also more overhead and memory
- Tune by load testing
--tensor-parallel-size
- Number of GPUs used to shard the model
- Example: tensor_parallel_size=2 for 2 GPUs
- Needed for larger models that don’t fit in one GPU

6.3 Example: benchmark-style load test

Using Python asyncio and the OpenAI client, you can simulate many concurrent users:

import asyncio
from openai import AsyncOpenAI
import time

client = AsyncOpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",
)

async def one_request(i: int):
    t0 = time.time()
    resp = await client.chat.completions.create(
        model="mistralai/Mistral-7B-Instruct-v0.2",
        messages=[{"role": "user", "content": f"Request {i}: Explain vLLM briefly."}],
        max_tokens=64,
        temperature=0.7,
    )
    dt = time.time() - t0
    text = resp.choices[0].message.content.strip()
    print(f"[{i}] Latency: {dt:.2f}s, Output: {text[:60]}...")
    return dt

async def main():
    concurrency = 128
    tasks = [one_request(i) for i in range(concurrency)]
    latencies = await asyncio.gather(*tasks)
    print(f"Average latency: {sum(latencies)/len(latencies):.2f}s")

if __name__ == "__main__":
    asyncio.run(main())

Run this while monitoring nvidia-smi:

watch -n 1 nvidia-smi

You should see high GPU utilization and stable memory usage.

6.4 Batch generation with Python API

If your app can batch at the client side (e.g., a microservice doing request-level aggregation), vLLM will happily handle it:

from vllm import LLM, SamplingParams

llm = LLM(
    model="mistralai/Mistral-7B-Instruct-v0.2",
    dtype="bfloat16",
)

sampling_params = SamplingParams(max_tokens=64, temperature=0.7)

prompts = [
    "Explain PagedAttention in simple terms.",
    "How does vLLM differ from naive inference?",
    "When should I scale to multiple GPUs with vLLM?",
    # add many more prompts...
]

outputs = llm.generate(prompts, sampling_params)

for prompt, out in zip(prompts, outputs):
    print("PROMPT:", prompt)
    print("OUTPUT:", out.outputs[0].text.strip())
    print("-" * 40)

vLLM will automatically:

Create a large batch
Schedule them efficiently on the GPU
Use PagedAttention to handle KV cache across all sequences

6.5 Multi-GPU and larger models

For models that don’t fit on a single GPU (e.g., 70B parameters), use tensor parallelism:

# Example for a 70B model on 4 GPUs:
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-2-70b-chat-hf \
  --tensor-parallel-size 4 \
  --dtype bfloat16 \
  --gpu-memory-utilization 0.9 \
  --max-model-len 4096 \
  --host 0.0.0.0 \
  --port 8000

Notes:

vLLM will distribute the model across GPUs
All GPUs must be visible (e.g., CUDA_VISIBLE_DEVICES=0,1,2,3)
Interconnect bandwidth (NVLink/PCIe) can become a factor at very high throughput

7. Common Pitfalls and How to Avoid Them

7.1 Mismatched CUDA / PyTorch / vLLM versions

Symptom: Import errors, segfaults, or CUDA initialization failures.

Avoidance:

Install PyTorch with a specific CUDA version first (e.g., cu118/cu121)
Match vLLM’s recommended versions from the GitHub README
If in doubt, start from a clean virtualenv or Docker image

7.2 Running out of GPU memory (OOM)

Symptoms: Process killed, CUDA OOM errors, or silent crashes.

Common causes:

Using a model too large for your GPU
Setting --max-model-len too high
Running with very high concurrency and/or large max_tokens

Mitigations:

Reduce --max-model-len to realistic values for your app
Lower --gpu-memory-utilization slightly if hitting edge OOMs
Use a smaller model or more GPUs (tensor parallelism)
Limit request parameters on the client side:
- Enforce max prompt length
- Enforce max generation length

7.3 Ignoring batching opportunities

Symptom: Low GPU utilization, surprisingly low throughput.

Causes:

Synchronous, one-request-at-a-time client logic
Not enough concurrency in load

Fixes:

Use async clients
Enable streaming only when necessary (streaming has overhead but improves perceived latency)
For batch jobs (offline scoring), explicitly batch prompts on the client

7.4 Extremely long prompts

Symptom: Latency and memory usage explode for certain requests.

LLMs are quadratic in sequence length for attention, and KV cache grows linearly with context length. Even with PagedAttention, very long contexts (e.g., 32k+) must be handled with care.

Mitigations:

Use retrieval-augmented generation (RAG) with chunked contexts
Trim conversation histories when appropriate
Set server-side limits on input length

7.5 Incorrect dtype

Symptom: Needlessly high memory usage or instability.

float32 is almost always unnecessary for inference
vLLM typically runs best with:
- bfloat16 on modern GPUs (A100, H100, L4, etc.)
- or float16 on slightly older architectures

Check model compatibility and use:

--dtype bfloat16

--dtype float16

7.6 Overfitting config to benchmarks, not real traffic

It’s easy to tune vLLM for a specific synthetic benchmark and then see worse performance in production.

Best practice:

Record a representative request distribution (prompt lengths, max_tokens, concurrency)
Replay that distribution for load testing
Tune --max-model-len, --max-num-seqs, and concurrency based on realistic workloads

8. Best Practices for Production vLLM Deployments

8.1 Containerization and reproducibility

Use Docker images for consistent environments
Pin versions:
- vllm==x.y.z
- torch==x.y.z
Include a simple health-check endpoint via your own sidecar or gateway

8.2 Observability

Log:
- Request/response metadata (tokens in/out, latency)
- GPU utilization and memory usage
Integrate with:
- Prometheus/Grafana for metrics
- OpenTelemetry or similar for tracing

Example high-level metrics to track:

Tokens/sec (throughput)
Average/percentile latency per request
GPU memory usage and utilization
Error rates (timeouts, OOMs)

8.3 Capacity planning

Roughly, throughput is limited by:

Model size (params, layers)
GPU type (TFLOPS, memory)
Context length

Rules of thumb:

For chat workloads with moderate context (2–4K tokens), expect good throughput on a single modern GPU for 7B–13B models
For long-context or very large models (34B+), expect:
- Lower concurrency per GPU
- Greater benefit from multi-GPU setups

Run your own benchmarks instead of relying on generic numbers.

8.4 API design

Design your client-facing API to help vLLM:

Enforce maximums for:
- Prompt length
- max_tokens
Offer default but configurable sampling parameters
Consider:
- temperature and top_p defaults for consistency
- Streaming vs non-streaming trade-offs

8.5 Isolation and multi-tenancy

If multiple teams or apps share vLLM:

Use separate vLLM instances per critical workload when possible
At minimum, enforce per-tenant:
- Rate limits
- Max tokens per minute
Consider QoS tiers:
- Low-latency tier with smaller model and strict limits
- Bulk tier for batch processing

9. Conclusion

vLLM gives you a specialized, highly optimized engine for LLM inference:

PagedAttention makes KV cache management efficient, letting you serve many concurrent users without wasting GPU memory.
The Python API and OpenAI-compatible server make it straightforward to integrate into your existing code and tools.
With a few tuning knobs (model size, dtype, max context, concurrency), you can reach high throughput and predictable performance.

For developers building LLM-backed products—chatbots, code assistants, RAG systems, internal tools—vLLM is one of the best open-source options to turn powerful models into fast, cost-effective services.

10. High-Quality Learning Resources and Links

A short, curated list to go deeper:

vLLM GitHub (primary reference)
https://github.com/vllm-project/vllm
- Installation instructions
- Configuration flags
- Examples (Python, server, multi-GPU, LoRA, etc.)
vLLM Documentation
https://docs.vllm.ai/
- Architecture overview
- Detailed guides on PagedAttention, scheduling, and deployment
- API reference and advanced features
PagedAttention Paper (Original Research)
Efficient Memory Management for Large Language Model Serving with PagedAttention
https://arxiv.org/abs/2309.06180
- Formal description of PagedAttention
- Benchmarks vs other systems
vLLM Blog / Release Notes (Performance Insights)
- Linked from GitHub Releases and docs
- Contains performance improvements, new features, and real-world benchmarks
Hugging Face + vLLM Integration Guides
https://huggingface.co/docs/text-generation-inference/en/vllm (or related integration pages)
- How to use vLLM with popular open models
- Practical examples for code, chat, and RAG scenarios

With these resources and the patterns from this tutorial, you should be well-equipped to go from zero to production-grade LLM serving with vLLM.

Introduction#

1. What Is vLLM and Why Does It Matter?#

1.1 What is vLLM?#

1.2 Why does vLLM matter?#

2. PagedAttention in Simple Terms#

2.1 How KV cache usually works#

2.2 The analogy: from big arrays to paged memory#

2.3 How PagedAttention works (intuitively)#

2.4 Why PagedAttention is fast#

3. Installing vLLM#

3.1 Prerequisites#

3.2 Basic installation via pip#

4. Your First vLLM Example (Python API)#

4.1 Minimal generation example#

4.2 Notes on this example#

5. Serving Models with vLLM’s OpenAI-Compatible API#

5.1 Start the API server#

5.2 Call the server using curl (Chat Completions)#

5.3 Call the server with the OpenAI Python SDK#

6. Using vLLM for High-Throughput Inference#

6.1 Core ideas for high throughput#

6.2 Tuning important server parameters#

6.3 Example: benchmark-style load test#

6.4 Batch generation with Python API#

6.5 Multi-GPU and larger models#

7. Common Pitfalls and How to Avoid Them#

7.1 Mismatched CUDA / PyTorch / vLLM versions#

7.2 Running out of GPU memory (OOM)#

7.3 Ignoring batching opportunities#

7.4 Extremely long prompts#

7.5 Incorrect dtype#

7.6 Overfitting config to benchmarks, not real traffic#

8. Best Practices for Production vLLM Deployments#

8.1 Containerization and reproducibility#

8.2 Observability#

8.3 Capacity planning#

8.4 API design#

8.5 Isolation and multi-tenancy#

9. Conclusion#

10. High-Quality Learning Resources and Links#