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 performanceRTX 4090 — Previous generation, 24GB vRAM, great valueRTX PRO 6000 — Professional grade, 48GB vRAM, ideal for large models
Rental Type
On-Demand — Standard pricing, no interruption riskInterruptible — 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 pricing3 months — 10% discount6 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 – 0– 0– 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 optionPrice (High → Low) — Find premium hardwareGPU Count — See multi-GPU servers firstMax 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 (48GB) or 2–4x 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:
Click Rent Now on the offer card
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:
Template Includes Best For Ubuntu 22.04 Base Clean Ubuntu with NVIDIA drivers Custom setups Ubuntu 24.04 Base Latest Ubuntu with NVIDIA drivers Custom setups (latest packages) Stable Diffusion (A1111) + Jupyter Automatic1111 WebUI, Jupyter Image generation ComfyUI + Jupyter ComfyUI node editor, Jupyter Advanced image workflows 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. 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 hourDaily — Projected 24-hour costWeekly — Projected 7-day costMonthly — Projected 30-day costReserved 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.
