vLLM stands for virtual large language model. It’s an open-source library that makes serving large language models (LLMs) faster and cheaper.

Think of LLMs like powerful brains in AI systems. They need lots of GPU power to run queries. vLLM optimizes this process, helping enterprises handle more requests without breaking the bank.

Built at UC Berkeley’s Sky Computing Lab, vLLM was created to fix key issues in LLM serving. Traditional methods waste GPU memory and slow down under load. vLLM uses smart techniques like PagedAttention to change that.

This guide covers everything from basics to advanced features. Whether you’re a developer or business leader, you’ll see why vLLM is gaining traction in AI infrastructure.

vLLM is an open-source inference and serving engine for LLMs. It boosts throughput and cuts memory waste, making LLM deployment easier and more cost-effective. Unlike traditional frameworks, it handles high loads efficiently on various hardware like NVIDIA and AMD GPUs.

Developed in 2023 at UC Berkeley, vLLM started as a research project. It aimed to solve memory bottlenecks in AI serving. Now, it’s community-driven with over 50,000 GitHub stars.

vLLM supports popular models from Hugging Face. It offers an OpenAI-compatible API for seamless integration.

LLMs exploded in popularity after models like GPT. But serving them at scale was tough.

Developers faced high costs and slow responses. GPU memory was often wasted, limiting user capacity.

vLLM was built to democratize AI. It makes high-performance LLM serving accessible, even for teams with limited resources. By optimizing memory and batching, it slashes inference costs by 50-70%.

Serving LLMs isn’t simple. These models process vast data, leading to unique challenges.

GPU Memory Inefficiency LLMs store “memories” called KV caches during queries. These grow with input length, eating up GPU space. Traditional systems allocate big chunks upfront, wasting 60-80% of memory due to fragmentation—like reserving a whole hotel floor for one guest.

Throughput Bottlenecks Handling multiple users means batching requests. But fixed batches wait for all to finish, slowing everything. This creates delays in real-time apps.

Dynamic Batching vLLM uses continuous batching. It adds or removes requests mid-process, like a conveyor belt that never stops. This maximizes GPU use.

PagedAttention: Simplified Explanation PagedAttention is vLLM’s secret weapon. It treats KV caches like pages in a book, not one long scroll.

It divides caches into small blocks. These blocks aren’t stuck together in memory. A “block table” maps them, allocating only what’s needed.

Analogy: Imagine memory as apartment units. Traditional methods rent whole buildings per tenant. PagedAttention rents rooms on demand, sharing common areas. This cuts waste to under 4%.

vLLM processes LLM requests in two phases: prefill (initial computation) and decode (token generation). It uses PagedAttention for memory management and continuous batching for efficiency. This setup runs on GPUs, supporting distributed inference for scale.

Here’s a step-by-step:

• Step 1: Load Model – Pull from Hugging Face or similar. vLLM quantizes weights to save space.
• Step 2: Handle Request – User sends prompt. vLLM adds to batch queue.
• Step 3: Prefill Phase – Compute initial KV cache in parallel.
• Step 4: Decode Phase – Generate tokens autoregressively, using cached data. Continuous batching keeps GPU busy.
• Step 5: Output – Stream responses via API.

This flow ensures low latency even under heavy load.

vLLM achieves 2-4x higher throughput than alternatives through PagedAttention and continuous batching. It minimizes memory waste, allowing larger batches and better GPU utilization. Benchmarks show up to 24x speedup over Hugging Face Transformers for batch workloads.

Is vLLM better than traditional serving?

Yes, vLLM outperforms traditional approaches like Hugging Face Transformers in throughput and efficiency. It handles dynamic loads better, reduces costs, and scales easily. Traditional methods struggle with memory fragmentation.

Here’s a comparison table:

vLLM shines in production. For example, it served millions at Chatbot Arena, cutting GPU use by 50%.

vLLM powers real-world AI.

• Chatbots and Assistants: Handles concurrent queries fast, like LinkedIn’s AI features.
• Content Generation: Speeds up summarization for legal or healthcare docs.
• Search Enhancement: Boosts retrieval-augmented generation (RAG) in enterprises.

Enterprises adopt vLLM for its OpenAI compatibility and hardware flexibility. Companies like Roblox use it for moderation, Amazon for shopping assistant Rufus, and LinkedIn for gen AI tools.

Efficient serving is key for AI agents—autonomous systems that plan and act. vLLM enables quick responses in multi-step tasks.

In enterprise automation, it streamlines workflows like data analysis or customer support.

Scalable AI platforms benefit too. Low-code/no-code tools integrate vLLM for optimized serving, letting non-tech users build AI without performance hits.

This reduces costs and boosts productivity across sectors.

About the Author

Pradeep Chandran

Pradeep Chandran is a seasoned technology leader and a key contributor at lowtouch.ai, a platform dedicated to empowering enterprises with no-code AI solutions. With a strong background in software engineering, cloud architecture, and AI-driven automation, he is committed to helping businesses streamline operations and achieve scalability through innovative technology.

At lowtouch.ai, Pradeep focuses on designing and implementing intelligent agents that automate workflows, enhance operational efficiency, and ensure data privacy. His expertise lies in bridging the gap between complex IT systems and user-friendly solutions, enabling organizations to adopt AI seamlessly. Passionate about driving digital transformation, Pradeep is dedicated to creating tools that are intuitive, secure, and tailored to meet the unique needs of enterprises.