Enable Neural Frame Rate Upscaling in Unreal Engine
Introduction
Understand neural graphics and NFRU
Set up the development environment
Set up the Unreal project with NFRU enabled
Validate NFRU in Unreal Engine
Understand console variables for NFRU
Set up a debug view in Unreal Engine
Use RenderDoc for Arm GPUs with NFRU for debugging and analysis
Next Steps

  Log an issue

  Fork and edit

  Discuss on Discord

About this Learning Path

Skill level:  Introductory
Reading time:  30 min
Last updated:  29 Oct 2026
Skill level:
  Introductory
Reading time:
  30 min
Last updated:
  29 Oct 2026
Author:Powen Yang, Arm
Arm IP:Mali Immortalis
Tags: ML Windows NX Unreal Engine Vulkan SDK Visual Studio
Author:
  • Powen Yang, Arm
Arm IP:
Mali Immortalis
Tags:
ML Windows NX Unreal Engine Vulkan SDK Visual Studio

Who is this for?

This Learning Path is designed for developers interested in exploring neural frame generation techniques in Unreal Engine® using ML extensions for Vulkan®.

What will you learn?

After completing this Learning Path, you'll be able to perform the following tasks:

  • Understand the fundamentals of neural graphics in game development
  • Set up and use ML extensions for Vulkan emulation
  • Set up Neural Frame Rate Upscaling (NFRU) in Unreal Engine
  • Visualize neural frame generation result
  • Set available console variables for tuning NFRU performance and quality

Prerequisites

Before starting, you'll need the following prerequisites:

  • Windows 11
  • Unreal Engine 5.4 or 5.6, with the Templates and Feature Pack enabled
  • Visual Studio, with Desktop Development with C++ and .NET desktop build tools
  • Git Large File Storage (LFS) downloaded and installed

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll clone the Arm Neural Graphics plugin, download the Vulkan ML emulation layers, and add the plugin to an Unreal Engine C++ project. After configuring Vulkan, you’ll test NFRU in Standalone Game or a packaged Windows build. You’ll use console variables, the debug view, frame-generation statistics, and RenderDoc for Arm GPUs to inspect and tune NFRU.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which Unreal Engine project type should I create to use NFRU?
Create a Third Person template project using the C++ option. You need a C++ project to build the Arm Neural Graphics plugin.
Why doesn’t NFRU work in the standard Unreal Editor viewport?
NFRU isn’t supported in the standard viewport. Use Standalone Game mode or create a packaged build to run and validate NFRU.
Where should I place the Neural Graphics plugin in my project?
Create a Plugins directory in your project root, then copy the complete neural-graphics-for-unreal folder into it. Keep the downloaded emulation-layer files inside the plugin folder.
Which Vulkan SDK version do I need for ML extension emulation?
Install Vulkan SDK version 1.4.321.0 or newer. Use Vulkan Configurator to enable the emulation layers for running ML workloads through Vulkan ML extensions.
How do I tune or debug NFRU while testing?
In Standalone Game or a packaged Development build, use r.NFRU.Enable, r.NFRU.ShowDebugView, and r.NFRU.PaceAdjuster to enable NFRU, view intermediate buffers, and tune frame pacing.
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