> ## Documentation Index
> Fetch the complete documentation index at: https://docs.deepinfra.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Deploy Private Models

> Run your own LLMs and image models on dedicated GPU infrastructure with autoscaling.

DeepInfra allows you to deploy your own models on dedicated infrastructure — your weights, your endpoint, your isolation.

## Why run private models?

* **Compliance** — data stays on dedicated infrastructure, not shared with other users
* **Custom weights** — deploy fine-tuned or trained-from-scratch models
* **Predictable latency** — no sharing with other users means consistent response times
* **Autoscaling** — scale from 0 to many instances automatically based on load
* **Competitive GPU pricing** — some of the lowest per-GPU-hour rates available, with no lock-in
* **Simple deployment** — up and running in just a couple of clicks from the dashboard

## What you can deploy

<CardGroup cols={2}>
  <Card title="Custom LLMs" icon="robot" href="/private-models/custom-llms">
    Deploy any Hugging Face LLM on A100/H100/H200/B200/B300 GPUs with the OpenAI-compatible API.
  </Card>

  <Card title="LoRA Adapters" icon="sliders" href="/private-models/lora">
    Deploy LoRA fine-tuned language models on top of supported base models.
  </Card>

  <Card title="LoRA Image Models" icon="image" href="/private-models/lora-image">
    Deploy LoRA adapters for image generation from Civitai.
  </Card>
</CardGroup>

## GPU options

Private model deployments run on:

* **A100-80GB** — proven workhorse for LLM inference, great value
* **H100-80GB** — fast and widely supported
* **H200-141GB** — large HBM3e memory, ideal for big models
* **B200-180GB** — NVIDIA Blackwell, significantly faster for inference workloads
* **B300-288GB** — latest NVIDIA Blackwell Ultra, highest performance available

## Pricing model

Unlike shared inference (pay per token), private deployments are billed per GPU-hour. You pay for the time your GPUs are running, regardless of traffic.

<Warning>
  Leaving a custom deployment running by mistake can rack up costs quickly. For example, forgetting to shut down a 2-GPU deployment over a weekend (64 hours) costs \~\$256 USD. Always set spending limits in [payment settings](https://deepinfra.com/dash/billing).
</Warning>

## Getting started

1. Go to [Dashboard → Deployments](https://deepinfra.com/dash/deployments)
2. Click **New Deployment**
3. Choose your deployment type (Custom LLM, LoRA, or LoRA Image)
4. Fill in the configuration and deploy

See the specific guides for each deployment type:

* [Custom LLMs](/private-models/custom-llms)
* [LoRA Adapters](/private-models/lora)
* [LoRA Image Models](/private-models/lora-image)