Try vLLM
Overview
vLLM is the engine, not the application. When an organisation decides to serve an open-weight model to real users at real volume, this is overwhelmingly what runs underneath — the layer that turns a set of model weights into an endpoint that can answer thousands of concurrent requests without falling over or wasting most of the GPU it is running on.
Its reputation rests on PagedAttention, a memory-management technique borrowed conceptually from operating system virtual memory. Naive inference servers waste enormous amounts of GPU memory on fragmented key-value cache; PagedAttention allocates it in pages, which raises the number of concurrent requests a given GPU can serve by a large multiple. Continuous batching does the complementary job on the scheduling side, keeping the GPU fed rather than idling between requests.
It is emphatically infrastructure. There is no interface, no model browser, no chat window — you get an OpenAI-compatible API server and a set of flags, and you are expected to know what you are doing with GPUs. That is the correct design for what it is, and it is why the recommendation is sharply bimodal: essential if you are serving a model to many users, entirely unnecessary if you are not.
Key Features
PagedAttention Memory Management
Allocates key-value cache in pages rather than contiguous blocks, dramatically raising the concurrent requests a single GPU can handle.
Continuous Batching
Requests join and leave batches dynamically instead of waiting for a batch boundary, which keeps the GPU busy and latency predictable under load.
OpenAI-Compatible Server
Exposes the OpenAI API shape, so existing clients and tooling work against your self-hosted model without a rewrite.
Multi-GPU and Distributed Serving
Tensor and pipeline parallelism for models too large for one card, which is most frontier open-weight models in 2026.
Apache 2.0 Licence
Permissive licensing with no commercial restrictions, which matters for products built on top.
Broad Model Support
Supports the major open-weight families quickly after release, so new models are usually deployable within days.
Pros & Cons
Advantages
- Order-of-magnitude better GPU utilisation than naive serving
- The de-facto standard, so documentation and deployment recipes are everywhere
- OpenAI-compatible API means no client rewrites
- Apache 2.0 with no commercial restrictions
- New open-weight models are supported quickly
Disadvantages
- Requires real GPU and distributed systems knowledge
- No user interface at all — it is infrastructure
- Configuration and tuning have a steep learning curve
- Complete overkill for single-user local inference
Pricing Plans
| Plan | Price | Key Features |
|---|---|---|
| Open Source | Free | Apache 2.0, self-hosted; you pay for GPUs |
Best Use Cases
vLLM Excels At:
- Serving an open-weight model to many concurrent users
- Cutting inference cost versus per-token hosted APIs at volume
- Deployments where model weights must stay on your infrastructure
- Backing a self-hosted platform such as Open WebUI at team scale
May Not Be Ideal For:
- Single-user local inference — LM Studio or Ollama are the right tools
- Teams without GPU infrastructure expertise
- Low request volumes where a hosted API is cheaper all-in
How It Compares
vLLM vs Ollama
Ollama is for running a model on your machine; vLLM is for serving a model to your users. They are not alternatives — most teams end up using Ollama locally and vLLM in production.
vLLM vs a hosted API
Below a certain volume, a hosted API is cheaper once you count GPU rental and engineering time. Above it, vLLM wins decisively — which is why cost modelling should come before the deployment decision, not after.
Final Verdict
Our Recommendation
vLLM is the correct answer to a specific question: how do we serve an open-weight model in production without wasting most of our GPU budget? PagedAttention and continuous batching are genuine engineering advances, not marketing, and the resulting utilisation gap over naive serving is what makes self-hosting economically viable at all. It demands real infrastructure competence and gives you nothing resembling a product. Model the cost against a hosted API honestly before committing — including engineer time — and if self-hosting wins, this is what you run.