Three-dimensional content has become integral to gaming, films, digital twins, product design and augmented reality. Traditional 3D modeling workflows are labor‑intensive and slow; however, recent advances in generative models like Neural Radiance Fields (NeRF), 3D Gaussian Splatting and diffusion-based approaches promise to automate and accelerate 3D creation. Businesses across industries are exploring these tools to build immersive experiences, simulate environments and reduce prototyping costs.

This guide explores the rapidly evolving world of generative 3D, highlighting market growth, cutting-edge techniques and practical deployment on Runpod’s cloud GPUs. We’ll discuss how 3D Gaussian Splatting enables real-time rendering, examine distributed training to overcome single-GPU limitations, and show how Runpod empowers you to experiment with generative 3D models without expensive hardware. Whether you’re a designer, researcher or game developer, the future of 3D starts here.

Why generative 3D matters

The demand for 3D content is skyrocketing. Market research indicates that generative AI is projected to reach a valuation of about $45 billion by 2023, while the 3D mapping and modeling market may grow from $7.48 billion in 2023 to $14.82 billion by 2029. Generative 3D models promise to revolutionize how we create an d interact with virtual objects by automating design and enabling rapid iteration. Companies like NVIDIA, Autodesk, Microsoft and Meta are investing heavily to integrate generative AI into 3D production pipelines.

Generative 3D also offers tangible business benefits: reduced time-to-market, material and labor savings, and the ability to generate countless design variants. Industries from manufacturing to real estate and healthcare are leveraging these models for predictive maintenance, virtual tours, animation and personalized medicine. Adopting generative 3D today positions your organization at the forefront of innovation.

Key techniques: NeRFs and 3D Gaussian Splatting

Neural Radiance Fields (NeRF)

NeRFs represent scenes as continuous 3D volumes that can be rendered from arbitrary viewpoints. Traditional NeRF training required hours on a high-end GPU, limiting practical applications. However, NVIDIA’s Instant NeRF implementation introduced a multi-resolution hash grid and a compact network architecture that achieves over 1,000× speedups compared with previous methods. Instant NeRF can train on a few dozen photographs in seconds and render new viewpoints in milliseconds, democratizing photorealistic 3D reconstruction. According to the Instant NGP FAQ, a usable NeRF begins to emerge within five minutes of training on a single GPU.

3D Gaussian Splatting (3DGS)

An alternative technique, 3D Gaussian Splatting, exploded in popularity in 2023 for real-tim e rendering. Instead of representing a scene as a volumetric field, 3DGS models it as a set of Gaussians with position, scale, color and opacity. This anisotropic representation, combined with tile-based rasterization, enables high-quality radiance field rendering in real time. 3DGS excels at large-scale scenes like urban landscapes, where traditional NeRFs struggle with memory requirements.

Researchers observed that training 3D Gaussian Splatting on a single GPU limits the number of Gaussians and thus the fidelity of the reconstruction. A recent study introduced Grendel, a distributed training system that partitions the computation across multiple GPUs. By distributing 40.4 million Gaussians across 16 GPUs, the system produced higher-quality results (greater PSNR) compared with training 11.2 million Gaussians on one GPU. This demonstrates that scaling up hardware directly translates to improved model quality and faster convergence.

Using generative 3D on Runpod

Runpod offers the perfect environment to experiment with generative 3D models. Here’s how you can get started:

1. Choose a GPU. For NeRF and 3DGS training, GPUs with high memory and compute – such as the A100 and H100 – are recommended. Access them on-demand via Runpod’s Cloud GPUs. For development and smaller scenes, more affordable GPUs (e.g., A6000 or RTX 6000 Ada) suffice. Runpod’s spot instances provide cost-effective access when you’re iterating or perf orming background training.
2. Set up your environment. You can use open-source implementations like instant-ngp for Instant NeRF or ksplat for 3D Gaussian Splatting. Build a Docker image containing the necessary libraries (CUDA, PyTorch, NVIDIA OptiX for NeRF) and your dataset. Runpod’s Serverless platform also enables you to expose inference endpoints for your trained models.
3. Scale training. If you want to train large 3DGS models, leverage Runpod’s Instant Clusters. You can spin up clusters of up to 64 H100 GPUs, distribute your dataset and training across nodes, and achieve quality improvements similar to the Grendel study. Runpod handles networking and management, letting you focus on your model.
4. Serve and visualize. Once trained, you can serve your NeRF or 3DGS model via a web API or embed it in a 3D viewer. Runpod’s pods support web servers and WebGL frameworks for interactive visualization. Alternatively, convert your model to formats like glTF or USD for integration with 3D editors.

Example applications

• Real estate and architecture: Convert 2D photographs into photorealistic 3D walkthroughs of homes or construction sites. Clients can explore spaces remotely, saving time and travel. Generative 3D also allows designers to visualize changes instantly and iterate on layouts.
• Manufacturing and industrial design: Rapidly generate and test design variations f or parts and assemblies. Generative models can optimize for weight, strength and material usage while respecting constraints, accelerating product development and reducing prototyping costs.
• Entertainment and gaming: Create immersive scenes for films and video games without manual modeling. NeRFs can reconstruct sets or actors using a small number of photographs, while 3DGS can handle large outdoor environments.
• Healthcare and medical imaging: Transform CT or MRI scans into 3D representations for planning surgeries or training. Generative 3D tools can synthesize variations or simulate tissue deformation, aiding research and education.

Best practices and tips

1. Start small. Experiment with low-resolution scenes and shorter training times to understand the workflow. You’ll get immediate feedback and can iterate quickly.
2. Optimize data collection. High-quality 3D reconstructions require well-lit photos with sufficient coverage. For NeRFs, capture images from many viewpoints; for 3DGS, ensure you have depth information if available.
3. Use mixed precision and caching. Enabling FP16 acceleration reduces memory usage and speeds up training. When rendering, use caching and progressive refinement to improve frame rates.
4. Balance cost and quality. Large scenes or high-resolution models require more compute. Use spot pods for experimentation and switch to on-demand pods when running critical training jobs. Monitor GPU utilization and adjust clust er size accordingly.
5. Integrate with Runpod Hub. Check the Runpod Hub for templates of NeRF and 3DGS applications. These community-contributed containers can accelerate your setup.

Frequently asked questions

Do I need multiple GPUs for generative 3D?

For smaller scenes, a single GPU suffices. Instant NeRF can produce high-quality results within minutes on one GPU. However, if you aim to train large 3DGS models or multi-view scenes at high resolution, distributing the workload across multiple GPUs yields better quality and faster training.

How long does it take to train these models?

Instant NeRF produces usable results within five minutes. Traditional NeRFs and 3DGS training can take hours to days depending on scene complexity and hardware. Using GPU clusters and efficient implementations like Instant NGP drastically reduces training time.

Are there licensing concerns?

Most open-source implementations are released under permissive licenses. Always check the license of the specific repository you use. If you intend to use generative models for commercial applications, ensure compliance with any content or data rights.

What file formats do these models output?

NeRF models often export as neural network checkpoints that require custom renderers. Some tools convert results into mesh formats (e.g., OBJ) or point clouds. 3DGS models output Gaussian sets that can be rasterized. You can convert both into universal format s like glTF or USD for broader compatibility.

Can I integrate generative 3D with VR/AR applications?

Yes. Once trained, you can render scenes in real time and stream them to VR/AR headsets. With Runpod’s GPU clusters, you can host high-fidelity 3D experiences remotely and stream them to user devices with minimal latency.

Conclusion

Generative 3D models are transforming how we design, visualize and interact with digital worlds. Techniques like Instant NeRF and 3D Gaussian Splatting unlock real-time photorealistic rendering, while distributed training over multiple GPUs enhances quality and reduces time. The market is poised for explosive growth, and pioneers are already leveraging these tools to innovate across industries.

Runpod offers the infrastructure you need to explore this frontier. With on-demand GPU access, per-second billing and instant cluster deployment, you can experiment with generative 3D models without investing in expensive hardware. From photorealistic real estate tours to next-generation game worlds, the possibilities are endless.

Join the 3D revolution today. Sign up for Runpod and start creating, training and serving generative 3D models in the cloud. Whether you’re a solo developer or part of an enterprise, Runpod puts cutting-edge 3D AI within reach.