A guide to AI inference hosting on dedicated servers and VPS

Running inference models in production is a key part of delivering machine learning applications at scale. Unlike model training, which relies on GPU-heavy infrastructure, inference typically requires fast CPUs, low latency, and consistent performance. This makes dedicated servers and high-performance VPS compelling alternatives to public cloud platforms.

In this guide, we explore how to host inference models effectively on a VPS for AI workloads or a dedicated server for machine learning, with a focus on performance, scalability, and bandwidth flexibility.

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What is AI inference?

Inference is the phase in the machine learning lifecycle where a trained model is used to make real-time predictions on new data. This can range from image recognition and text classification to fraud detection and recommendation systems.

Unlike training, which is compute-intensive and sporadic, inference is often latency-sensitive and continuous, especially in production environments.

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Why use a VPS or dedicated server for inference?

While cloud-hosted inference can be convenient, many developers and businesses are turning to self-managed infrastructure for better control, lower costs, and consistent performance.

1. Dedicated compute resources

A VPS or dedicated server ensures that CPU, RAM, and storage are not shared with other tenants, critical for maintaining consistent response times and uptime.

2. Predictable costs with unmetered bandwidth

Cloud services often charge based on usage, especially bandwidth. Hosting on an unmetered VPS for AI inference allows you to transfer unlimited data at a fixed monthly cost, which is ideal for cost control on high-traffic or data-heavy applications.

3. Greater control over deployment

Self-hosting offers full control over OS, libraries, storage, and access policies. This can simplify compliance with data protection regulations or internal security policies.

4. Low latency and high throughput

AI inference models may need to serve thousands of predictions per second. High-throughput networking and fast I/O are essential for real-time performance.

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Key infrastructure considerations

When choosing a VPS for AI workloads or a dedicated server for inference, here’s what to look for:

CPU performance

Multi-core processors (e.g. AMD EPYC, Intel Xeon) are ideal for parallel processing, allowing the server to handle multiple inference requests simultaneously.

Sufficient memory

Memory should be sized to load the model fully into RAM for optimal speed, especially for large language or image models.

NVMe SSD storage

Fast storage helps reduce latency when loading models or working with large datasets. NVMe drives offer significantly higher IOPS than SATA SSDs.

Unmetered bandwidth

Inference services often need to respond to global traffic, stream data, or deliver media-rich responses. High bandwidth with no data cap is optimal for scalability and user experience.

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Common use cases for AI inference hosting

• Hosting REST APIs for model inference
• Image or object recognition at the edge
• Real-time NLP applications (chatbots, text classifiers)
• Recommendation systems in e-commerce
• Audio or video processing
• Lightweight deployment of transformer models using ONNX or TensorRT

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Final thoughts: When to consider FDC

If you're deploying models that need consistent performance, high throughput, and cost-effective bandwidth, running inference on a dedicated server or unmetered VPS can provide a solid foundation.

At FDC, we offer:

• Flat-rate unmetered bandwidth
• High-core-count CPUs optimized for inference loads
• Fast NVMe storage
• Multiple global locations for lower latency delivery

Whether you’re running lightweight models or serving thousands of predictions per second, our infrastructure is built to support scalable AI inference hosting with full control and no surprise bills.