Install Model Gym and explore neural graphics examples

What is neural graphics?

Neural graphics is an intersection of graphics and machine learning. Rather than relying purely on traditional GPU pipelines, neural graphics integrates learned models directly into the rendering stack. These techniques are particularly powerful on mobile devices, where battery life and performance constraints limit traditional compute-heavy rendering approaches. Your goal is to deliver high visual fidelity without increasing GPU cost. You achieve this by training and deploying compact neural networks optimized for your device’s hardware.

How does Arm support neural graphics?

Arm enables neural graphics through the Neural Graphics Development Kit : a set of open-source tools that let you train, evaluate, and deploy ML models for graphics workloads.

At its core are the ML Extensions for Vulkan, which bring native ML inference into the GPU pipeline using structured compute graphs. These extensions (VK_ARM_tensors and VK_ARM_data_graph) allow real-time upscaling and similar effects to run efficiently alongside rendering tasks.

You can develop neural graphics models using well-known ML frameworks like PyTorch, then export them for deployment with Arm’s hardware-aware pipeline. The workflow converts your model to .vgf using the TOSA intermediate representation, making it possible to tailor model development for your game use case. In this Learning Path, you will focus on Neural Super Sampling (NSS) as the primary example for training, evaluating, and deploying neural models using the Neural Graphics Model Gym . To learn more about NSS, see the resources on Hugging Face . Arm has also developed a set of Vulkan Samples to help you get started. The .vgf format is introduced in the postprocessing_with_vgf sample. For a broader overview of neural graphics developer resources, including the Vulkan Samples, see the introductory Learning Path Get started with neural graphics using ML Extensions for Vulkan .

Starting in 2026, Arm GPUs will feature dedicated neural accelerators, optimized for low-latency inference in graphics workloads. To help you get started early, Arm provides the ML Emulation Layers for Vulkan that simulate future hardware behavior, so you can build and test models now.

What is the Neural Graphics Model Gym?

The Neural Graphics Model Gym is an open-source toolkit for fine-tuning and exporting neural graphics models. It is designed to streamline the entire model lifecycle for graphics-focused use cases, like NSS.

With Model Gym, you can:

• Train and evaluate models using a PyTorch-based API
• Export models to .vgf using ExecuTorch for real-time use in game development
• Take advantage of quantization-aware training (QAT) and post-training quantization (PTQ) with ExecuTorch
• Use an optional Docker setup for reproducibility

You can choose to work with Python notebooks for rapid experimentation or use the command-line interface for automation. This Learning Path will walk you through the demonstrative notebooks and prepare you to start using the CLI for your own model development.

You’re now ready to set up your environment and start working with neural graphics models. Keep going!