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

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About this Learning Path

Skill level:  Introductory
Reading time:  30 min
Last updated:  03 Aug 2026
Skill level:
  Introductory
Reading time:
  30 min
Last updated:
  03 Aug 2026
Author:Powen Yang
Arm IP:Mali Immortalis
Tags: ML Windows NX Unreal Engine Vulkan SDK Visual Studio
Author:
  • Powen Yang
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?

Upon completion of this Learning Path, you will be able to:

  • 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 will need the following:

  • 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 configure NFRU in Unreal Engine using Arm’s Neural Graphics Development Kit and ML Extensions for Vulkan. You’ll build the Neural Graphics SDK, enable the Vulkan ML emulation layers, and add the Neural Rendering plugin to a C++ project. After configuring Vulkan as the rendering hardware interface, you’ll validate NFRU in Standalone Game mode and inspect frame-generation statistics and intermediate buffers. You’ll then learn to adjust NFRU settings with console variables, visualize NFRU intermediate buffers, and inspect frames with RenderDoc for Arm GPUs to examine Vulkan events, resources, and pipeline stages. By the end, you’ll have a working NFRU project and a repeatable workflow for validation and debugging.

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. A C++ project is required to build the Neural Graphics for Unreal 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 the project’s root directory to place the plugin. Then, create a symbolic link to the plugin folder for your Unreal Engine version. You need administrator permissions to create the symbolic link.
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?
Adjust NFRU console variables, such as r.NFRU.Enable, r.NFRU.ShowDebugView, and r.NFRU.DataGraphFrameGeneration, in Standalone Game mode to control enablement, debugging, performance tuning, and frame generation modes.
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