How to Deploy vLLM for High-Concurrency Inference

vLLM has quickly become the go-to engine for serving large language models at scale — and the difference between a sluggish deployment and one that handles thousands of concurrent requests almost always comes down to how it’s set up. This guide explains how to deploy vLLM for high-concurrency inference, covering hardware planning, installation, and production hardening. If you’re still deciding on infrastructure, HostingB2B’s LLM hosting give you dedicated NVIDIA GPUs with full driver-level access, built specifically for running models like this in production — so you can scale from a single card to multi-GPU nodes without re-architecting later. The steps below apply to single-GPU and multi-GPU setups alike.

What Is vLLM?

vLLM is an open-source engine built for fast, memory-efficient LLM serving. It uses PagedAttention to manage the key-value cache and continuous batching to keep GPUs busy. As a result, throughput improves noticeably. See the official vLLM documentation for technical depth, which is why how to deploy vLLM correctly matters for production.

Related ReadHow to Install CUDA on Ubuntu for Dedicated GPU Infrastructure

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Why vLLM for Production Serving

Continuous batching lets vLLM group incoming requests dynamically, so GPU utilisation stays high under variable load. Meanwhile, PagedAttention reduces memory fragmentation, allowing more concurrent sequences per GPU. Consequently, teams see higher throughput and lower latency, which is why vLLM is a standard choice for vllm production deployment at scale.

Step-by-Step Deployment Guide

Step 1: Provision a Dedicated GPU Server

First, match hardware to model size: smaller models run well on one V100S 32GB card, while larger models need an H100 for high concurrency. HostingB2B’s LLM hosting offer both, so provisioning the right infrastructure is the first step in how to deploy vLLM successfully.

Step 2: Install vLLM

Next, follow a simple vllm install guide: install with pip install vllm, or use a vllm docker setup that pulls the official image from the vLLM GitHub repository. Docker is generally simpler for repeatable deployments across servers.

Step 3: Launch the OpenAI-Compatible API Server

Then, launch it with the vllm serve command (vllm serve <model>), which exposes a vllm openai compatible api instantly. Therefore, existing OpenAI SDK clients connect without code changes.

Step 4: Multi-GPU Serving with Tensor Parallelism

For larger models, set the vllm tensor parallel size flag to split the model across GPUs, for example –tensor-parallel-size 2 on two V100S 32GB GPU servers from EUR 983/mo. As a result, memory constraints ease, although GPU-to-GPU bandwidth should also be considered.

Step 5: Load Test and Tune Throughput

Afterward, benchmark under realistic concurrency and adjust max-num-seqs, KV cache size, and max-model-len. Consequently, you find the latency-throughput balance that suits your workload instead of guessing.

Step 6: Run It 24/7 with systemd, Health Checks, and TLS

Finally, wrap the server in a systemd unit for automatic restarts, add health checks, and terminate TLS at a reverse proxy. Together, these turn a test deployment into a resilient service.

Comparison Table: GPU and VRAM Planning per Model Size

Common Use Cases by Industry

Industries adopt vLLM differently: SaaS firms use it for customer chat, fintech firms use it for document analysis under strict controls. Similarly, iGaming platforms use it for real-time player support, and healthcare providers use it for internal tools where data residency matters most.

Enterprise Deployment Considerations

For regulated workloads, serving from UK/EU data centres helps meet residency expectations, since data stays in the region. Additionally, enterprises should plan for redundancy, monitoring, and access control around the inference server, not only the model. Backups of configuration and logs matter too, given high uptime expectations for production LLM services.

FAQ Section

Conclusion

In summary, how to deploy vLLM well means matching hardware to model size, tuning concurrency, and hardening the service for production, not just running one command. With the right GPU infrastructure, teams serve models reliably at scale. See LLM hosting for available configurations.