vLLM Deployment Services for Your Business
vLLM deployment services run open models on your own GPUs with PagedAttention memory management and continuous batching, so many requests are served at once. Omeiro sets up the server with an OpenAI-compatible API, tunes batching and measures throughput.
How vllm deployment works, step by step
A made-up example of one run, from start to finish. Each step is logged so you can see what happened.
- Something happens
A form, an email, a payment or a new row.
- The workflow runs
Steps you agreed, in the right order.
- Errors are caught
Failed steps retry or alert a person.
- You see what ran
A simple log of every run.
Sample Made-up names, data and steps to show how a run is logged. Your steps, tools and rules follow your business.
What you get with vllm deployment
Production model serving utilizing vLLM and PagedAttention memory optimization. Achieving 10x-24x token throughput improvements and concurrent request batching on NVIDIA GPUs.
Basic Package
VLLM server for 1 model on 1 GPU, OpenAI-compatible API. 4-day delivery, 1 round + 30-day fixes.
Standard Package
Batching tuning, multi-GPU, auth, monitoring, throughput benchmark. 8-day delivery, 1 round + 30-day fixes.
Premium Package
Up to 3 models, Kubernetes deploy, autoscaling, 30 days support. 14-day delivery, 1 round + 60-day fixes.
Clear brief first
We confirm what you need before work starts, so the scope is clear on both sides.
Revisions listed
Each package shows its revision rounds. A round is one list of changes sent together.
Yours once paid
Copyright of the final work passes to you once the order is paid in full.
Third-party tools, API usage and hosting are billed to your own accounts, so the setup stays with your business.
How our vllm deployment works
Choose the model and GPUs
We size the setup to your traffic.
Install and expose
vLLM is configured with an OpenAI-style endpoint.
Tune
Batch sizes and memory settings are adjusted.
Benchmark
Throughput is measured under load.
vLLM deployment: sizing GPUs, context length and concurrency
Memory is the first constraint. vLLM deployment must fit model weights plus the key-value cache that grows with each active request, so long context windows and many simultaneous users consume memory fast. Before ordering, state the model, the longest prompts you expect and how many people will use it at peak. Those numbers show whether one GPU is enough or whether splitting the model across several is needed.
Tuning follows measurement. A vLLM deployment improves throughput through continuous batching, but settings such as maximum sequence length and batch size trade latency against capacity, so we benchmark with traffic that looks like yours. Security matters too: an OpenAI-compatible endpoint should sit behind authentication and network limits. Decide how new model versions will be rolled out, and keep a rollback path, so upgrades do not interrupt people who depend on the service.
In short
- Delivery in 4 to 14 days, by package.
- Fixed price, shown before you order.
- Teams hosting open models and finding throughput too low.
- Developers wanting a vllm inference setup behind an OpenAI-style API.
- Copyright yours after full payment.
Vllm Deployment: common questions
How much does vllm deployment cost?
Prices are fixed and shown by package on our Vllm Deployment pricing page, so you see the full price before you order. You can also ask for a custom quote.
How long does vllm inference setup take?
Delivery depends on the package: Basic 4-day delivery; Standard 8-day delivery; Premium 14-day delivery.
What is included in the Basic Package?
The Basic Package includes: vLLM server for 1 model on 1 GPU, OpenAI-compatible API. Higher packages add more scope, revisions and support.
Can I get a custom high throughput LLM serving package?
Yes. If none of the packages fits, use the custom package form on our pricing page to describe what you need, and our team sends you a fixed price.
vLLM or Ollama?
Ollama suits single users and desktops. vLLM suits many concurrent users.
Which models run on it?
Many popular open models. Name yours before ordering.
Can it autoscale?
Premium adds Kubernetes and autoscaling.
Is the API compatible with OpenAI clients?
Yes, on every tier.
What is PagedAttention?
It is vLLM's method of storing the attention cache in small blocks, much like virtual memory, which wastes less GPU memory. That lets the server hold more concurrent requests.
Who owns the final work?
Copyright of the final work passes to you once the order is paid in full. Paid fonts, stock files or licences, if used, are licensed in your name.
Ready to start your vllm deployment?
Compare the packages and pick the one that fits. Not sure which? Write to us and we will help you choose.