---
title: "AI & Machine Learning Infrastructure"
description: "Machine learning infrastructure at the edge. GPU compute for training and low-latency inference — run LLMs on distributed GPUs with zero egress fees."
url: https://edge.network/solutions/ai/
---

# AI & Machine Learning Infrastructure

AI & Machine Learning

# Inference is better at the edge

Run LLMs and SLMs on distributed GPU infrastructure. Low-latency inference,
cost-effective training, and global scale.

[Get Started](https://edge.network/console) [View GPU Pricing](https://edge.network/compute/gpus)

## Purpose-Built for AI Workloads

Infrastructure designed from the ground up for machine learning.

### GPU Compute at the Edge

NVIDIA GPUs deployed globally for low-latency inference. Run models closer to your users.

### Model Hosting

Deploy and scale ML models with automatic load balancing and version management.

### Sub-100ms Inference

Edge deployment means faster responses. Critical for real-time AI applications.

### Global Distribution

Serve AI workloads from 60+ locations. Automatic routing to the nearest GPU cluster.

### Flexible Infrastructure

From shared GPUs to dedicated clusters. Scale compute up or down as demand changes.

### Cost-Effective Training

Access GPU compute at a fraction of hyperscaler prices. No egress fees for model deployment.

## Built for Every AI Use Case

### Real-Time Inference

Deploy models for instant predictions – image recognition, NLP, recommendations.

### LLM Applications

Host and serve large language models with low latency and high throughput.

### Computer Vision

Process video and images at the edge for surveillance, quality control, and more.

### Training Workloads

Access affordable GPU compute for model training and fine-tuning.

## Why Edge for AI?

Traditional cloud providers charge premium prices for GPU compute and add steep egress fees.
Edge offers a better way.

Up to 60% lower GPU costs vs. hyperscalers
Zero egress fees for model deployment
Global edge locations for low-latency inference
Simple, predictable pricing
No long-term commitments required

Example Savings

### A100 GPU Instance

AWS p4d.24xlarge $32.77/hr
Google Cloud a2-highgpu-8g $29.39/hr
Edge GPU Compute $12.00/hr

[View full GPU pricing](https://edge.network/compute/gpus)

## Machine learning infrastructure, without the hyperscaler premium

Most ML infrastructure lives in a handful of centralised regions. That works for
batch training, but it breaks down for inference: every request pays a round trip
to a distant data centre, and every response pays egress fees on the way out.
Running models at the edge — on [GPU compute](https://edge.network/compute/gpus) distributed
close to your users — cuts both the latency and the bill.

Edge provides the full stack: GPU instances for training and fine-tuning,
low-latency inference close to users, [S3-compatible object storage](https://edge.network/storage) for
models and datasets with zero egress fees, and a [global CDN](https://edge.network/cdn) for serving
results. Deploy open models in minutes with our ready-made stacks
for [Ollama](https://edge.network/solutions/ollama), [vLLM](https://edge.network/solutions/vllm), [llama.cpp](https://edge.network/solutions/llama-cpp) and [ComfyUI](https://edge.network/solutions/comfyui).

New to the topic? Start with our guide to
[machine learning at the edge](https://edge.network/academy/machine-learning-at-the-edge).

## Ready to deploy AI at the edge?

Get started with GPU compute today. No commitment, pay only for what you use.

[Get Started](https://edge.network/console) [Talk to an Expert](https://edge.network/contact)