Choosing a GPU Instance

How to browse the marketplace, compare GPU offers, and pick the right instance for your workload.

Finding the right GPU instance depends on your workload, budget, and how long you need it. This guide walks you through the Nova Cloud marketplace and helps you make the best choice.

Browsing the Marketplace

When you log into the console, the Dashboard shows all available GPU offers in a card-based grid. Each card represents a server with one or more GPUs available to rent.

Understanding Offer Cards

Each offer card shows everything you need to know at a glance:

Field	What It Means
GPU	The GPU model and count (e.g., “4x RTX 5090”)
Price	Hourly rate — discounted price shown if filtering by reserved
CPU	Processor model and core count
RAM	System memory (not GPU memory)
vRAM	Total GPU memory across all GPUs
Disk	Available storage capacity
PCIe	PCIe generation (Gen 5.0 = fastest GPU-to-CPU bandwidth)
Datacenter	Physical location of the server
Availability	Whether the offer can be rented right now

Offer Sources

You’ll see two types of offers:

• Nova Cloud — Managed directly by Nova Cloud. Click Rent Now to create an instance.
• Nova Cloud Partners — Third-party marketplace offers from trusted partners.

Using Filters

The filter panel lets you narrow down offers to find exactly what you need.

GPU Family

Filter by GPU model:

• RTX 5090 — Latest generation, 32GB vRAM, best performance
• RTX 4090 — Previous generation, 24GB vRAM, great value
• RTX PRO 6000 — Professional grade, 96GB vRAM, ideal for large models

Rental Type

• On-Demand — Standard pricing, no interruption risk
• Interruptible — Lowest price, can be preempted

See the Rental Types guide for details on each type.

Reservation Period

When browsing, you can filter by reservation period to see discounted pricing:

• None — Pay-as-you-go pricing
• 3 months — 10% discount
• 6 months — 20% discount

Offer cards will show the discounted price with a percentage badge when a reservation filter is active.

Spec Filters

Fine-tune results based on your technical requirements:

Filter	Range	Use When
GPU Count	1–8 minimum	Multi-GPU training (e.g., distributed fine-tuning)
Min vRAM	0–1024 GB	Large model loading (e.g., 70B+ parameter models)
Min System RAM	0–1024 GB	Data preprocessing, large datasets
Min Disk Space	0–16384 GB	Large datasets or model checkpoints
Min CPU Cores	0–256	CPU-intensive preprocessing
Max Hourly Price	$0–$128	Budget constraints

Search

Use the search bar to filter by GPU name, server ID, or datacenter location.

Sorting

Sort results to surface the best options:

• Price (Low → High) — Find the cheapest option
• Price (High → Low) — Find premium hardware
• GPU Count — See multi-GPU servers first
• Max RAM — Find memory-rich configurations

Choosing the Right GPU

By Workload

Workload	Recommended GPU	Why
Fine-tuning 7B–13B models	1x RTX 4090 (24GB)	Enough vRAM for LoRA/QLoRA fine-tuning
Fine-tuning 30B–70B models	1x RTX PRO 6000 (96GB) or 4x RTX 4090	Need more vRAM for larger models
Stable Diffusion / ComfyUI	1x RTX 4090 or RTX 5090	Fast image generation with good vRAM
LLM inference (production)	1x RTX 5090 (32GB)	Best throughput for serving
Large-scale training	4–8x RTX 4090 or RTX 5090	Distributed training across GPUs
3D rendering	1x RTX 5090	Latest architecture, best RT cores

By Budget

Budget	Strategy
Lowest cost	Use Interruptible rental type with auto-restart enabled. Best for fault-tolerant training with checkpointing.
Best value	Use Reserved with a 3–6 month commitment for 10–20% off. Best for long-running workloads.
Maximum flexibility	Use On-Demand. Pay more per hour but stop anytime with no commitment.

Creating Your Instance

Once you’ve found the right offer:

1. Click Rent Now on the offer card
2. You’ll be taken to the Create Instance page with the GPU pre-selected

Choose an OS Template

Select a pre-configured environment or start fresh:

ID	Template	Includes	Best For
100	Ubuntu 22.04 Base	Clean Ubuntu with NVIDIA drivers	Custom setups
101	Ubuntu 24.04 Base	Latest Ubuntu with NVIDIA drivers	Custom setups (latest packages)
102	Stable Diffusion (A1111) + Jupyter	Automatic1111 WebUI, Jupyter	Image generation
103	ComfyUI + Jupyter	ComfyUI node editor, Jupyter	Advanced image workflows
104	Linux Desktop + Jupyter	Desktop environment, Jupyter	GUI-based work

Templates with “Jupyter” include a WebUI portal you can access from the console — no SSH required. See the Connecting guide for details. The ID column is the value to pass as template_vmid when creating instances via the API.

Configure Storage

Choose your disk size based on your needs:

Use Case	Recommended Storage
Basic development	50–100 GB
Model fine-tuning	100–250 GB
Large datasets	500–1000 GB
Multiple large models	1000+ GB

Storage is billed separately from GPU time and continues while the VM is stopped. You cannot add storage after you have created the instance, so ensure you choose enough storage for your task.

Set Up Authentication

Choose how you’ll connect:

• SSH Key — Select one of your uploaded keys (recommended). See the SSH Keys guide if you haven’t uploaded one yet.
• Password — Set a strong password (12+ characters, mixed case, numbers, symbols).

Select a Rental Type

Choose between On-Demand, Interruptible, or Reserved. The cost estimate updates in real-time as you change options. See the Rental Types guide for a detailed comparison of each type.

Review & Create

The cost estimate panel shows your projected costs:

• Hourly — GPU + storage cost per hour
• Daily — Projected 24-hour cost
• Weekly — Projected 7-day cost
• Monthly — Projected 30-day cost
• Reserved deposit — Upfront amount (if choosing reserved)

Click Create and wait 1–10 minutes for your instance to be provisioned.

What’s Next?

Connect to Your Instance

Learn how to access your VM via SSH or WebUI.

Instance Ports

Open ports for web services, Jupyter, and APIs.

Rental Types

Deep dive into on-demand, interruptible, and reserved pricing.

Billing

Understand pricing, auto billing, and invoices.