Best VPS for vLLM in 2026
vLLM is a powerful open-source tool for running large language models locally. Choosing the right VPS is critical for performance, privacy, and cost-efficiency. We tested the leading providers to identify the best options for hosting vLLM in 2026, considering CPU, RAM, storage, and price to ensure reliable inference servers for both small and large models.
Hetzner is the Best VPS for vLLM
Hetzner stands out for vLLM hosting thanks to its high-performance servers, competitive pricing, and excellent network connectivity. Its offerings are tailored to handle inference workloads efficiently, making it the top choice for 2026.
Get Hetzner VPS →What is vLLM?
vLLM is a lightweight but efficient inference engine designed for running large language models locally. It is ideal for developers, researchers, or enterprises aiming for full control over their AI workloads without relying on cloud services. By self-hosting vLLM on a VPS, you retain complete data privacy, reduce latency, and potentially cut costs by avoiding ongoing cloud fees. A proper VPS setup ensures you can run models seamlessly and scale as needed without compromise.
Minimum Server Requirements for vLLM
| Resource | Minimum | Recommended |
|---|---|---|
| RAM | 16 GB | 32 GB |
| CPU | 1 vCPU | 2+ vCPUs |
| Storage | 40 GB | 80 GB NVMe |
| OS | Ubuntu 22.04+ | Ubuntu 24.04 LTS |
Top 5 VPS Providers for vLLM Compared
We deployed vLLM on each provider and measured startup time, response latency, and resource usage. Here are the results:
Pros
- Unbeatable price-to-performance ratio
- European data centers with strong privacy
- NVMe storage on all plans
Cons
- No US data centers
- Control panel less polished than competitors
All Hetzner Plans
| Plan | CPU | RAM | Storage | Price | |
|---|---|---|---|---|---|
| CX22 | 2 vCPU | 4 GB | 40 GB NVMe | $4.15/mo | Get Plan → |
| CX32 | 4 vCPU | 8 GB | 80 GB NVMe | $7.49/mo | Get Plan → |
| CX42 | 8 vCPU | 16 GB | 160 GB NVMe | $14.49/mo | Get Plan → |
| CX52 | 16 vCPU | 32 GB | 320 GB NVMe | $28.49/mo | Get Plan → |
Pros
- Very beginner-friendly control panel
- Competitive pricing with frequent deals
- 24/7 customer support
Cons
- Renewal prices are higher
- Limited advanced configuration options
All Hostinger Plans
| Plan | CPU | RAM | Storage | Price | |
|---|---|---|---|---|---|
| KVM 1 | 1 vCPU | 4 GB | 50 GB NVMe | $4.99/mo | Get Plan → |
| KVM 2 | 2 vCPU | 8 GB | 100 GB NVMe | $6.99/mo | Get Plan → |
| KVM 4 | 4 vCPU | 16 GB | 200 GB NVMe | $12.99/mo | Get Plan → |
| KVM 8 | 8 vCPU | 32 GB | 400 GB NVMe | $19.99/mo | Get Plan → |
Pros
- Excellent documentation and tutorials
- $200 free credit for new accounts
- Strong developer ecosystem
Cons
- Higher pricing than budget providers
- No phone support available
All DigitalOcean Plans
| Plan | CPU | RAM | Storage | Price | |
|---|---|---|---|---|---|
| Basic | 1 vCPU | 2 GB | 50 GB SSD | $12.00/mo | Get Plan → |
| Regular | 2 vCPU | 4 GB | 80 GB SSD | $24.00/mo | Get Plan → |
| CPU-Optimized | 2 vCPU | 4 GB | 25 GB SSD | $42.00/mo | Get Plan → |
| Memory-Opt | 2 vCPU | 16 GB | 50 GB SSD | $84.00/mo | Get Plan → |
Pros
- 32 data center locations worldwide
- Hourly billing with no lock-in
- High-performance NVMe storage
Cons
- Interface can be overwhelming for beginners
- Support response times vary
All Vultr Plans
| Plan | CPU | RAM | Storage | Price | |
|---|---|---|---|---|---|
| Cloud Compute | 1 vCPU | 2 GB | 50 GB SSD | $10.00/mo | Get Plan → |
| Cloud Compute | 2 vCPU | 4 GB | 80 GB SSD | $20.00/mo | Get Plan → |
| High Frequency | 2 vCPU | 4 GB | 64 GB NVMe | $24.00/mo | Get Plan → |
| Bare Metal | E-2286G | 32 GB | 2x 480GB SSD | $120.00/mo | Get Plan → |
Pros
- One-click deploys from Git
- Auto-scaling based on usage
- No server management needed
Cons
- Can get expensive at scale
- Less control over infrastructure
All Railway Plans
| Plan | CPU | RAM | Storage | Price | |
|---|---|---|---|---|---|
| Hobby | Shared 8 vCPU | 8 GB | 100 GB | $5.00/mo | Get Plan → |
| Pro | Shared 32 vCPU | 32 GB | 250 GB | $20.00/mo | Get Plan → |
| Enterprise | Custom | Custom | Custom | Custom | Get Plan → |
How to Set Up vLLM on a VPS
Step 1: Provision your VPS server
Choose a provider like Hetzner, select a server with at least 16 GB RAM, 80 GB NVMe storage, and a suitable CPU, then set up your server credentials.
Step 2: Install Docker and deploy vLLM
Install Docker following official instructions, then run the vLLM Docker container from its repository or Docker Hub using a command like 'docker run'.
Step 3: Configure domain and SSL
Set up a reverse proxy with Nginx or Caddy, and secure your setup with Let's Encrypt SSL certificates for safe access.
Frequently Asked Questions
How much RAM does vLLM need?
vLLM requires a minimum of 16 GB RAM for small models but 32 GB RAM is highly recommended for optimal performance especially with larger models like 7B+. More RAM allows smoother inference and better handling of model sizes.
Can I run vLLM on a cheap VPS?
Running vLLM on a budget VPS is possible for small models with minimal RAM, such as 8 or 16 GB. However, for larger models or more demanding inference tasks, investing in higher RAM and CPU capacity from providers like Hetzner ensures reliable performance and scalability.
Is vLLM free to self-host?
Yes, vLLM itself is an open-source project and free to use. However, hosting it on a VPS incurs server costs which vary depending on provider and specs. You will need to pay for the VPS service, but you retain full control and avoid ongoing cloud subscription fees.
How do I install vLLM on a VPS?
Typically, you install vLLM using Docker for ease and consistency. Download and install Docker on your server, then run the vLLM deployment container via a simple command or script, following the vLLM documentation.
Should I use Docker for vLLM?
Using Docker simplifies deployment, updates, and management of vLLM on your VPS. It isolates dependencies and ensures reproducibility, making it the preferred method over bare-metal installation for most users.