# Quick Start

### Overview

#### What is GPU Virtual Machine?

**GPU Virtual Machine** (**GPU VM**) enables you to **deploy and manage high-performance GPU servers** with ease. **GPU VM uses a passthrough GPU to get a dedicated GPU**, applications access it through the layers of a guest OS and hypervisor. Other critical VM resources that applications use, such as RAM, storage, and networking, are also virtualized.

FPT AI Factory portal currently offers Local NVMe storage with each GPU instance.

This fixed-capacity storage is optimized for high-performance training workloads but is not suitable for long-term data retention.For persistent data storage with on-demand scaling capabilities, Block Storage – Persistent Disk is available through FPT Cloud by contacting FPT Support.

#### GPU VM vs GPU Container

|                               | **GPU VM**                                                                                                                                                                                                     | **GPU Container**                                                                                                               |
| ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- |
| **Environment Control**       | <ul><li><strong>Full OS + root access</strong>(SSH, sudo)</li><li>Install <strong>custom drivers, CUDA versions, and system libs</strong></li><li>Debug low-level issues (NCCL, networking, drivers)</li></ul> | <ul><li><strong>Workload runs inside Docker</strong></li><li>Don’t care about the host OS, Pre-configured environment</li></ul> |
| **Setup Speed**               | Slower to set up (Spin-up VMs & Software)                                                                                                                                                                      | Starts in 1-2 minutes                                                                                                           |
| **Workload Type**             | <ul><li>Long-running experiments</li><li>LLM training (multi-GPU)</li><li>Fine-tuning with custom libs</li></ul>                                                                                               | <ul><li>Fast experiments</li><li>Inference microservices</li><li>Batch inference jobs</li></ul>                                 |
| **Networking & System Needs** | <ul><li>Full network control</li><li>Custom ports, routing, SSH</li><li>Easier multi-node debugging</li></ul>                                                                                                  | <ul><li>Depends on platform networking</li><li>Extra abstraction layers</li></ul>                                               |
| **Billing model**             | NVMe **storage is retained**; **full charge while stopped**                                                                                                                                                    | NVMe\*\* storage is cleared\*\*; **no charge while stopped** (excluding persistent storage)                                     |

### Quick start

#### Step 1: Sign up and Sign in

* Go to [https://ai.fptcloud.com](https://console.fptcloud.com/) or [https://ai.fptcloud.jp](#scroll-bookmark-4), click **Sign Up**, and follow the system instructions to enter your details.
* Our support team will contact you shortly to verify your information and activate your account.
* Sign in with your **FPT ID username/email and password**

#### Step 2: Add credit to account

1. Navigate to section ACCOUNT and click **Billing**
2. Click **Add Credit** button and enter an amount and payment information to complete.

Or, you have a voucher from FPT, apply your valid code in Add Voucher section to redeem credit

#### Step 3: Create a GPU VM

1. Select **GPU Virtual machine** in the Side menu.
2. Click button **Create New Virtual machine** and configure the VM deployment.
3. Follow the detailed guide here.

#### Step 3: Connect to GPU VM

1. In the **GPU VM list** page, click GPU VM name to access GPU VM details and the monitoring dashboard
2. Depends on your configurations in **Access GPU VM** section, choose one of the methods to connect: Web console with Root password or Terminal with SSH key
3. If you use the **default security group, all inbound and outbound traffic is allowed for this VM**.**We recommend updating rules to restrict access** to trusted IPs and the above exposed ports only (e.g., SSH 22, RDP 3389, HTTP/HTTPS).
4. Follow the detailed guide here.


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