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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.

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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.
Nova Cloud marketplace overview

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Understanding Offer Cards

Each offer card shows everything you need to know at a glance:
GPU offer card showing specs and pricing
FieldWhat It Means
GPUThe GPU model and count (e.g., “4x RTX 5090”)
PriceHourly rate — discounted price shown if filtering by reserved
CPUProcessor model and core count
RAMSystem memory (not GPU memory)
vRAMTotal GPU memory across all GPUs
DiskAvailable storage capacity
PCIePCIe generation (Gen 5.0 = fastest GPU-to-CPU bandwidth)
DatacenterPhysical location of the server
AvailabilityWhether the offer can be rented right now

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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.

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Using Filters

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

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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

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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.

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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.

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Spec Filters

Fine-tune results based on your technical requirements:
FilterRangeUse When
GPU Count1–8 minimumMulti-GPU training (e.g., distributed fine-tuning)
Min vRAM0–1024 GBLarge model loading (e.g., 70B+ parameter models)
Min System RAM0–1024 GBData preprocessing, large datasets
Min Disk Space0–16384 GBLarge datasets or model checkpoints
Min CPU Cores0–256CPU-intensive preprocessing
Max Hourly Price0–0–128Budget constraints
Use the search bar to filter by GPU name, server ID, or datacenter location.

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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

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Choosing the Right GPU

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By Workload

WorkloadRecommended GPUWhy
Fine-tuning 7B–13B models1x RTX 4090 (24GB)Enough vRAM for LoRA/QLoRA fine-tuning
Fine-tuning 30B–70B models1x RTX PRO 6000 (96GB) or 4x RTX 4090Need more vRAM for larger models
Stable Diffusion / ComfyUI1x RTX 4090 or RTX 5090Fast image generation with good vRAM
LLM inference (production)1x RTX 5090 (32GB)Best throughput for serving
Large-scale training4–8x RTX 4090 or RTX 5090Distributed training across GPUs
3D rendering1x RTX 5090Latest architecture, best RT cores

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By Budget

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

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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
Create instance configuration page

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Choose an OS Template

Select a pre-configured environment or start fresh:
IDTemplateIncludesBest For
100Ubuntu 22.04 BaseClean Ubuntu with NVIDIA driversCustom setups
101Ubuntu 24.04 BaseLatest Ubuntu with NVIDIA driversCustom setups (latest packages)
102Stable Diffusion (A1111) + JupyterAutomatic1111 WebUI, JupyterImage generation
103ComfyUI + JupyterComfyUI node editor, JupyterAdvanced image workflows
104Linux Desktop + JupyterDesktop environment, JupyterGUI-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.

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Configure Storage

Choose your disk size based on your needs:
Use CaseRecommended Storage
Basic development50–100 GB
Model fine-tuning100–250 GB
Large datasets500–1000 GB
Multiple large models1000+ 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.

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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).

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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.
Rental type selector showing pricing

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Review & Create

The cost estimate panel shows your projected costs:
Cost estimate breakdown
  • 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.

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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.