vLLM - How to Make Models Respond Faster Without Wasting Memory

Have you heard of vLLM? It’s one of the most important libraries in the world of large model inference. Its goal is simple: To speed up the token generation process - making models respond faster while using memory more efficiently.

Why Do We Even Need This?

When large models (LLMs) generate responses, they create tokens one by one. The problem is that a lot of work is often repeated - the system stores computations but doesn’t always know how to share them across different requests.

vLLM was built precisely to solve this: To reuse the same information repeatedly, without redundant computations and without wasting precious memory.

What Makes vLLM Special?

PagedAttention

A smart memory management mechanism that splits the KV Cache (memory of previous responses) into small pages. This allows sharing relevant parts of the memory across different requests, like an “efficient duplication” of intermediate results without storing everything twice.

Continuous Batching

Enables the system to accept new requests while processing existing ones - instead of waiting for the “batch” to finish. The result: the accelerator works continuously, with almost no idle time.

Extremely High Memory and Throughput Efficiency

Thanks to these two mechanisms, it’s possible to handle many more requests on the same GPU without losing speed or overloading memory.

Where Does It Fit?

vLLM is now integrated into many modern inference systems:

• API servers of AI companies.
• Systems like Hugging Face, OpenAI-compatible APIs.
• Integrations with Kubernetes or Ray Serve for large-scale deployment.

Bottom Line

vLLM makes the serving of large models truly efficient: Less waste, more speed, and the ability to handle hundreds of users simultaneously - on the same hardware.