3D rendering: AWS vs CUDO Compute

3D Rendering: AWS vs CUDO Compute

The increasing complexity of 3D rendering and motion graphics demands immense computational resources, often making cloud rendering a necessity rather than a choice. Highly detailed models and complex simulations can overwhelm standard local machines, particularly for tasks like photorealistic rendering and real-time effects.

Cloud rendering offers virtually unlimited computational power, allowing artists and studios to scale up as needed without investing in expensive hardware. However, choosing the right cloud computing platform is crucial. It can significantly impact project timelines, budgets, and the quality of your final output.

Source: Adobe

This article compares CUDO Compute and Amazon Web Service (AWS) on 3D rendering, focusing on hardware specifications, cost-effectiveness, scalability, software integration, and security measures.

While both platforms offer a plethora of hardware options, it's impractical to compare every possible combination in a single article. Therefore, we'll focus on the AWS G4 instance and a similar offering on CUDO Compute. Let's start by delving into their hardware specifications.

Hardware specifications and performance

The AWS G4 instances are available with a choice of NVIDIA GPUs (G4dn) or AMD GPUs (G4ad). The G4dn instances feature NVIDIA T4 GPUs and custom Intel Cascade Lake CPUs. These instances are designed for machine learning inference, video transcoding, and other graphics-intensive applications. The NVIDIA T4 GPUs are known for their performance in tasks such as real-time speech recognition and natural language processing.

G4 instances come in various configurations, with the lowest setup offering 4 vCPUs, 16 GB of RAM, and 125 GB of local NVMe SSD storage. The NVMe SSD storage in these instances is designed to provide high throughput and low latency, which are essential for data-intensive tasks.

On the other hand, CUDO Compute's platform is built to support high-performance computing tasks but does not use instances. Instead, with CUDO Compute, you have to select your configuration. The closest GPU configuration CUDO offers is the NVIDIA A5000.

Source: NVIDIA

The NVIDIA A5000 GPUs are particularly powerful, featuring 24 GB of GDDR6 memory and a robust Ampere architecture. They are well-suited for demanding workloads like 3D rendering and deep learning. The A5000, paired with AMD Epyc Zen 2 CPUs, provides an efficient solution.

In terms of performance, the NVIDIA A5000 is significantly better for video editing than the NVIDIA T4. Here's why:

Purpose and usage

The A5000 GPU includes RT Cores as part of its architecture, enhancing its real-time ray tracing ability, which is used in video editing, 3D rendering, and game development, where realistic lighting and reflections are important. The RT Cores allow the A5000 to handle complex visual effects and high-fidelity rendering tasks smoothly.

The T4 GPU also includes RT Cores, but they are less emphasized than its Tensor Cores, which are designed for AI and deep learning tasks. While the T4 can handle ray tracing to some extent, its RT Cores are not the primary focus, and it is not as powerful in visual computing tasks as the A5000. Instead, the T4 excels in scenarios where AI inference and deep learning are the primary workloads.

CUDA Cores and Tensor Cores

The NVIDIA A5000 features 8,192 CUDA cores and 256 Tensor Cores. These components are designed to handle parallel processing tasks efficiently, which are critical for GPU-accelerated video editing and 3D rendering. CUDA cores handle general-purpose computations that are accelerated by the GPU, which is why more CUDA cores typically translate into better performance in tasks like rendering, encoding, and effects processing.

On the other hand, the Tensor Cores are specialized for performing the matrix operations that are essential for AI tasks. Still, they also contribute to accelerating certain operations in video editing software, particularly those involving AI-driven features like denoising, upscaling, or AI-enhanced effects.

Source: NVIDIA

Comparatively, the NVIDIA T4 has significantly fewer CUDA cores (2,560) and Tensor Cores, making the A5000 better suited for workloads that benefit from extensive parallel processing.

The A5000’s higher number of CUDA and Tensor Cores allows it to handle more simultaneous calculations, making it ideal for tasks that require real-time rendering or fast processing of high-resolution video content.

VRAM

The NVIDIA A5000 is equipped with 24GB of GDDR6 VRAM, which allows it to handle large textures, complex 3D models, and high-resolution video files easily. The extra VRAM is particularly beneficial when working with 4K, 6K, or even 8K video, as well as when using intensive applications graphical computations like Blender or Adobe Premier Pro.

Source: NVIDIA

The NVIDIA T4 comes with 16GB of GDDR6 VRAM, which, while substantial, is less than the A5000’s capacity. This difference means that the T4 is more likely to encounter performance limitations when dealing with larger or more complex projects, particularly those that involve extensive GPU-accelerated tasks.

NVENC and NVDEC on the NVIDIA A5000

The NVIDIA A5000 includes dedicated hardware encoders (NVENC) and decoders (NVDEC), which are specifically designed to offload video encoding and decoding tasks from the GPU cores. This hardware acceleration allows for faster processing of video content, reducing the strain on the CPU and freeing up the GPU for other tasks such as rendering and effects processing.

These features are valuable in video editing and streaming, where they can significantly speed up rendering timelines, exporting videos, and transcoding media. For example, when exporting a project from Adobe Premiere Pro or DaVinci Resolve, NVENC can handle the encoding process much faster than software-based encoding methods.

Hardware-accelerated decoding (NVDEC) also enhances playback performance, allowing for smoother scrubbing and real-time preview of high-resolution footage, even when working with compressed formats like H.264, H.265 (HEVC), and others.

Source: NVIDIA

Applications that support GPU acceleration use these hardware encoders and decoders to optimize performance, allowing video editors render complex videos quickly or work in real-time with high-definition footage​.

Read more about the NVIDIA A5000 specifications here: NVIDIA RTX A5000: everything you need to know

Cost effectiveness and pricing models

The cost structures of AWS and CUDO Compute reflect their respective market strategies. AWS follows a more traditional pricing model that, while more expensive, provides extensive services and a high degree of support.

The AWS G4dn instances are available with multiple configurations (instance sizes) with single and multi GPU virtual machines (VMs). We will focus on two specific instance sizes, one for the single VM, and one for the multi VM instance.

The first of the single instances is the g4dn.xlarge which comprise of 1 GPU, 4 vCPUs with 16 GB of memory, and 125 GB SSD. The cost of this instance is $0.526 per hour on demand. The g4dn.12xlarge, which is a multi GPU VM features 4 GPU, 48 vCPUs with 192 GB memory, and 900 GB of NVMe SSD at $3.912 per hour on demand.

For CUDO Compute, the pricing is more flexible. Let’s use the A5000 and similar configurations to see how the pricing compares. For a VM with a single A5000 GPU, 4 vCPUs with 16 GB of memory, and 125 GB storage, it will cost $0.43815 per hour. Using a multi GPU VM, featuring 4 GPUs, 48 vCPUs with 192 GB memory, and 900 GB storage, it will cost $2.3413 per hour.

While the hourly cost differences might appear negligible, the cumulative effect over extended rendering projects can be substantial. The cost of rendering accumulates over time, and even a small price difference per hour can translate into significant savings when multiplied by the number of hours required to complete a project.

As the table illustrates, CUDO Compute's flexible pricing model and competitive rates can lead to substantial cost savings, particularly for large-scale or long-term rendering projects, allowing studios and artists to allocate their budgets more efficiently, investing in other aspects of their creative workflow.

When choosing a cloud rendering platform, it's essential to consider not only the upfront costs but also the long-term financial implications. CUDO Compute's cost-effectiveness, combined with its flexible configurations, makes it an attractive option for those seeking to optimize their rendering budgets without compromising on performance or quality. You can get started using the NVIDIA A5000 on CUDO Compute today!

Flexibility and scalability

AWS offers a highly reliable and globally distributed infrastructure, which is one of its core strengths. Its infrastructure allows users to scale operations seamlessly across multiple regions and availability zones, making it ideal for large, complex projects that require consistent performance and high availability.

AWS's services, such as EC2, Auto Scaling, and Elastic Load Balancing, enable users to efficiently handle varying workloads by automatically adjusting the number of running instances based on demand​. Its scalability is particularly advantageous for enterprises needing to support applications globally, ensuring low latency and redundancy.

Conversely, CUDO Compute relies on a decentralized model that uses underutilized computing resources from a global network. The approach allows it to offer rapid scalability, adapting quickly to changing project sizes and resource demands. The decentralized nature means that resources can be scaled up or down on demand, providing a flexible solution that is particularly cost-effective for projects with variable intensity and duration.

Unlike traditional centralized cloud providers like AWS, CUDO Compute’s model reduces costs by tapping into idle data center capacity worldwide, which can be particularly beneficial for users with fluctuating needs. This adaptive scaling capability makes it easy for users to adjust their resource usage dynamically, allowing them to only pay for what they use, which helps in managing budgets effectively​.

Both AWS and CUDO Compute offer excellent scalability, but they cater to different needs:

• AWS has a reliable, globally integrated infrastructure that suits large-scale, high-demand projects with consistent requirements.
• CUDO Compute provides a more flexible, cost-effective option for projects with variable needs, leveraging a decentralized approach that can rapidly scale resources according to demand.

Software integration and ecosystem

Software compatibility is crucial for rendering platforms. AWS boasts a broad ecosystem with extensive support for popular 3D rendering software like Maya and Cinema 4D. This is complemented by a comprehensive set of tools that facilitate a wide range of cloud computing tasks, from data processing to machine learning​.

CUDO Compute also provides strong support for major 3D software, including Blender, with optimizations for cloud-based GPU rendering. This focus on specialized software solutions helps reduce rendering times and improve project workflows, making it a strong contender for professionals focused on efficiency and performance.

Security and support

Security is a top priority for both platforms. AWS offers industry-leading security measures that comply with global compliance requirements, making it a safe choice for large companies handling sensitive data. Its customer support and extensive documentation ensure that users have all the necessary resources to secure their operations.

CUDO Compute emphasizes ease of use and accessibility, catering to a broader range of users, including smaller studios and freelancers. It offers substantial security measures but combines this with greater flexibility and lower cost.

Conclusion

Specific project requirements, budget constraints, and the desired level of scalability should guide the choice between AWS and CUDO Compute. AWS is suited for large-scale enterprises needing comprehensive service and global reach, whereas CUDO Compute offers a more cost-effective and flexible solution ideal for varying workload sizes and independent professionals in the rendering field.

Both platforms provide useful tools and infrastructures that significantly benefit the rendering and motion graphics industry, but their approaches cater to distinctly different market segments. CUDO Compute offers flexible cloud infrastructure for your VFX project. You can begin using a single or multiple NVIDIA A5000s with an operating system, storage, memory, network, and security customized by you for your project. You can start with the NVIDIA A5000 starting from $0.35 per hour. Get started today!