Deploy an LLM With vLLM on a Cloud GPU: Full Walkthrough
A hands-on tutorial for deploying an open large language model with vLLM on a cloud GPU, covering setup, serving, testing, and tuning for throughput.
vLLM has become one of the most popular ways to self-host large language models because it combines high throughput with an interface that mirrors familiar hosted APIs. If you can rent a GPU, you can stand up your own inference endpoint and serve an open model in well under an hour. This walkthrough takes you from a fresh cloud GPU to a working, OpenAI-compatible API, then covers the tuning knobs that decide how much throughput you get from the hardware you are paying for.
What vLLM Gives You
vLLM is an inference and serving engine for large language models. Its standout feature is efficient memory management for the attention mechanism, which lets it serve many concurrent requests on a single GPU without running out of memory. It also exposes an API that is compatible with the widely used OpenAI request format, so existing client code often works against your self-hosted endpoint with little more than a changed base URL.
The practical upshot is that you get control over which model you run, where it runs, and what it costs, while keeping the convenience of a standard API. That control is the main reason teams self-host rather than relying solely on hosted inference.
Step One: Provision a GPU
Choose a GPU with enough memory for your target model. Memory is the binding constraint: the model weights, the runtime, and the cache for in-flight requests all have to fit. As a rough guide, larger models need more GPU memory, and quantized versions of a model need less than their full-precision counterparts.
| Model size | Memory consideration |
|---|---|
| Small open model | Fits comfortably on a single mid-range GPU |
| Medium model | Needs an upper-tier GPU or quantization |
| Large model | May require a high-memory GPU or multiple GPUs |
For a first deployment, pick a small or medium open model so it fits on one GPU and you avoid the added complexity of multi-GPU sharding.
Step Two: Install vLLM
On a fresh GPU instance, install vLLM into a clean Python environment. Using a virtual environment keeps dependencies isolated and avoids conflicts with anything pre-installed on the image. After installation, confirm the GPU is visible to the runtime, because a missing or misconfigured driver is the most common early failure.
- Create and activate a clean Python environment.
- Install the vLLM package and its dependencies.
- Verify the GPU is detected before proceeding.
Step Three: Start the Server
vLLM ships with a server mode that launches an HTTP endpoint exposing the OpenAI-compatible API. You point it at a model identifier, and it downloads the weights and begins serving. The first launch takes longer because it fetches the model; subsequent launches reuse the cached weights and start much faster.
Once the server reports that it is ready, you have a live inference endpoint. It listens on a port on the instance, and you reach it either from the instance itself or, with the appropriate networking, from your local machine.
Step Four: Send a Test Request
Because the endpoint speaks the OpenAI format, testing it is straightforward. Send a chat-style request with a short prompt and confirm you get a coherent completion back. The key fields are the model name, the messages, and any generation parameters such as the maximum number of tokens to produce.
A successful response confirms the full path works: the server loaded the model, accepted the request, ran inference on the GPU, and returned text. From here, any client that can target a custom base URL can use your endpoint.
Step Five: Tune for Throughput
A working endpoint is the start, not the finish. The difference between a casual setup and an efficient one is how much work you extract from the GPU you are renting. Several settings control that.
- Continuous batching: vLLM batches incoming requests dynamically, which is the main reason it serves high concurrency well. Sending requests concurrently rather than one at a time lets this shine.
- GPU memory utilization: a setting controls how much of the GPU memory vLLM reserves for its cache. Raising it allows more concurrent requests, up to the point where you risk running out of memory.
- Maximum sequence length: capping context length conserves memory and improves the number of requests you can hold in flight.
- Quantization: a quantized model uses less memory, letting you fit a larger model on the same GPU or serve more concurrent requests, with a quality trade-off to evaluate.
The right combination depends on whether you optimize for latency on single requests or for throughput across many. For a shared endpoint serving an application, throughput usually wins, which favors higher concurrency and continuous batching.
Step Six: Measure What You Are Paying For
To know whether the deployment is cost-effective, measure throughput in requests or tokens per second and divide your GPU hourly rate by that throughput. This gives a cost per request or per token you can compare against hosted alternatives. A self-hosted endpoint at high utilization often undercuts hosted pricing, while the same endpoint at low utilization may cost more, which is why pushing concurrency matters for the economics, not just the performance.
Cleaning Up
When you finish, shut down the instance so billing stops. If you plan to redeploy, save the model cache to persistent storage so the next launch skips the download. Treat the running endpoint like any rented GPU: valuable while in use, pure cost when idle.
Production Considerations
A test endpoint and a production endpoint differ in a few important ways. For anything beyond experimentation, put the server behind a reverse proxy that handles authentication, since an open inference endpoint is an invitation to abuse and runaway cost. Add health checks so an orchestrator can detect a stalled server and restart it. Configure request timeouts so a single slow generation cannot tie up capacity indefinitely.
Scaling is the other major step. A single GPU serves a bounded number of concurrent requests, and beyond that, latency climbs as requests queue. When demand exceeds one GPU's comfortable throughput, run multiple replicas behind a load balancer, each on its own GPU, and distribute traffic across them. This horizontal approach is simpler and more robust than trying to squeeze ever more from one device, and it lets you scale capacity up and down with demand rather than paying for peak around the clock.
Choosing Between Self-Hosting and a Hosted API
vLLM makes self-hosting accessible, but it is not always the right call. Self-hosting wins when you have steady, high-volume traffic that keeps the GPU busy, when you need a specific open model, or when data residency and control matter. A hosted inference API often wins for spiky or low-volume traffic, because you pay per token with no idle GPU to fund. The cost per request you measured gives you the number to make this decision on evidence rather than preference.
Conclusion
Deploying an LLM with vLLM on a cloud GPU is a repeatable sequence: provision a GPU sized to the model, install vLLM in a clean environment, start the OpenAI-compatible server, test it, and tune concurrency and memory for throughput. The payoff is full control over your inference stack at a cost you can measure and optimize. Once the loop is familiar, you can spin up a dedicated endpoint for a new model in minutes and serve it on terms you set.