Picture this: you're firing up a large language model (LLM) for your chatbot app, and bam—your GPU memory is toast. Half of it sits idle because of fragmented key-value (KV) caches from all those user queries piling up. Requests queue up, latency spikes, and you're burning cash on extra hardware just to keep things running.
Sound familiar?
That’s the pain of traditional LLM inference, and it’s a headache for developers everywhere.
Enter vLLM, an open-source serving engine that acts like a smart memory manager for your LLMs. At its heart is PagedAttention, a clever technique that pages memory the same way an operating system does—dramatically reducing waste and boosting throughput.
In this post, we’ll dive deep into:
- Where vLLM came from
- How its core technology works
- Key features that make it shine
- Real-world wins
- How to get started in minutes
Whether you're building a production API or just experimenting locally, vLLM makes LLM serving easy, fast, and cheap.
What Is vLLM?
vLLM is a high-throughput, open-source library designed specifically for serving LLMs at scale. Think of it as the turbocharged engine under the hood of your AI inference server.
It handles:
- Concurrent request processing
- GPU memory optimization
- High-throughput token generation
At a glance, vLLM provides:
- A production-ready server compatible with OpenAI’s API specification
- A Python inference engine for custom integrations
The real magic comes from continuous batching and PagedAttention, which allow vLLM to pack far more requests onto a GPU than traditional inference engines. No more static batches waiting on slow prompts—requests flow in and out dynamically.
I’ve used vLLM myself to deploy LLaMA models, and it turned a sluggish single-A100 setup into a system pushing over 100+ tokens per second. If you’re tired of memory babysitting or horizontal scaling just to serve a handful of users, vLLM is your fix.
A Quick History of vLLM
vLLM emerged in 2023 from the UC Berkeley Sky Computing Lab. It was introduced in the paper:
vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention
Authored by Woosuk Kwon and team, the paper identified a massive inefficiency in LLM inference: KV cache fragmentation.
KV caches—temporary stores of attention keys and values—consume huge amounts of GPU memory during autoregressive generation. Traditional serving engines fragment this memory, leading to:
- Out-of-memory errors
- Underutilized GPUs
- Poor scalability
PagedAttention was introduced as the solution, borrowing ideas from virtual memory systems in operating systems.
The idea quickly gained traction. GitHub stars skyrocketed, community adoption exploded, and by mid-2023, vLLM was powering production workloads at startups and large tech companies alike.
By 2026, vLLM supports:
- 100+ models (LLaMA 3.1, Mistral, and more)
- Distributed serving with Ray
- Custom kernels and hardware optimizations
- Multi-LoRA and adapter-based serving
It’s a rare example of an academic idea scaling cleanly into real-world production.
Core Technology: PagedAttention and Continuous Batching
PagedAttention
PagedAttention is a non-contiguous memory management system for KV caches.
In standard Transformer inference:
- KV caches grow with sequence length
- Memory becomes fragmented
- New requests fail despite free memory existing
PagedAttention fixes this by:
- Splitting KV caches into fixed-size pages (e.g., 16 tokens)
- Storing them in a shared memory pool
- Tracking them using a logical-to-physical mapping table
The attention kernel gathers only the required pages at runtime—no massive copies, no fragmentation.
It’s essentially virtual memory for GPUs.
Continuous Batching
Traditional batching waits for a full batch to complete. If one request is slow, everything stalls.
vLLM uses continuous batching, where:
- Completed sequences drop out mid-batch
- New requests immediately take their place
- GPU utilization stays consistently high
Analogy:
Old batching is fixed seating in a restaurant.
vLLM is flexible seating—tables free up instantly when diners leave.
The result:
- Memory utilization jumps from 30–50% to over 90%
- Throughput improves by 2–4x
- Latency becomes predictable
Key Features That Make vLLM Stand Out
vLLM isn’t just about PagedAttention—it’s packed with production-grade features.
Quantization Support
Supports AWQ, GPTQ, FP8, and more. This reduces memory usage by 2–4x with minimal quality loss.
I’ve personally run a 70B model on two 40GB GPUs—something that was impossible before.
Tensor Parallelism
Seamlessly shards models across multiple GPUs using tensor parallelism. Scaling is near-linear up to 8+ GPUs.
Speculative Decoding
Uses a smaller draft model to propose tokens, which the main model verifies. This can double generation speed for interactive workloads.
Prefix Caching
Reuses KV caches for repeated prompts, ideal for chatbots and RAG pipelines with static system prompts.
Additional features include:
- OpenAI-compatible API
- Streaming responses
- JSON mode and tool calling
- Vision-language model support (e.g., LLaVA)
These optimizations stack. In benchmarks combining quantization, speculative decoding, and PagedAttention, vLLM exceeds 500 tokens/sec on H100 GPUs.
Benefits and Use Cases
Why Use vLLM?
Benchmarks consistently show:
- 2–4x higher throughput
- 24–48% memory savings
- Significantly lower infrastructure costs
On ShareGPT-style workloads, vLLM serves over 2x more requests per second than standard Hugging Face pipelines.
Common Use Cases
- Production APIs with OpenAI compatibility
- Retrieval-Augmented Generation pipelines
- Serving fine-tuned LoRAs and adapters
- On-prem or edge deployments
- Research and large-scale evaluation
A fintech team I know replaced TGI with vLLM for fraud detection. Throughput doubled, costs dropped 40%, and multi-tenancy became trivial.
Getting Started in 5 Minutes
Installation
pip install vllm
Start the Server
vllm serve meta-llama/Llama-2-7b-hf --port 8000
Your API is now live at:
http://localhost:8000/v1/chat/completions
Python Inference Example
from vllm import LLM, SamplingParams
llm = LLM(model="meta-llama/Llama-2-7b-hf")
prompts = ["Hello, world!", "Why is the sky blue?"]
sampling_params = SamplingParams(
temperature=0.8,
top_p=0.95,
max_tokens=100
)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
print(output.outputs[0].text)
Batching, memory management, and scheduling are all handled automatically.
vLLM vs Other Serving Engines
| Engine | Memory Efficiency | Throughput | Ease of Use |
|---|---|---|---|
| vLLM | High (PagedAttention) | 2–4x faster | High |
| Hugging Face TGI | Medium | Standard | Medium |
| TensorRT-LLM | High | Very High | Low |
vLLM offers the best balance of performance, usability, and flexibility—without vendor lock-in.
Final Thoughts
vLLM transforms LLM deployment from a resource-hungry problem into a scalable and affordable solution.
With:
- PagedAttention eliminating memory waste
- Continuous batching maximizing GPU utilization
- Advanced features like quantization and speculative decoding
vLLM is quickly becoming the backbone of modern LLM serving.
Whether you’re a solo developer or running large-scale AI infrastructure, vLLM makes high-performance inference accessible. Clone the repository, experiment locally, and keep an eye on upcoming releases—this space is moving fast.
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