Choose the Right GPU on Akamai Cloud for Your AI Workload

AI inference, natural language processing, computer vision, video analytics, rendering, and scientific visualization all rely on GPUs. The challenge is that not all GPU workloads look the same. Some require large memory for complex models. Some are dominated by real-time video processing. Others are driven by graphics, rendering, 8K streaming, transcoding, or certified design workflows.

Akamai Cloud supports all these workloads with three NVIDIA GPU options, allowing customers to precisely match infrastructure to their specific workload needs.

1. NVIDIA RTX PRO™ 6000 Blackwell Server Edition

2. NVIDIA RTX™ 4000 Ada Generation

3. NVIDIA QuadroⓇ RTX 6000 (legacy professional GPUs)

Each of these GPUs excels at different types of inference and graphics workloads. Choosing the right one maximizes performance, controls cost, and eliminates overprovisioning.

NVIDIA RTX PRO 6000 Blackwell Server Edition is a server-class GPU built on NVIDIA’s Blackwell architecture with very large GPU memory and high parallel processing capability. It is designed to power sustained, production AI inference; fine-tuning; 8K streaming; transcoding; and high-fidelity visualization workloads.

NVIDIA RTX 4000 Ada Generation is a workstation-class professional GPU based on NVIDIA’s Ada Lovelace architecture. It delivers exceptional price and performance; a balance of graphics performance, AI acceleration, power efficiency, and compact form factor; and modern AV1 video workflows.

NVIDIA Quadro RTX series GPUs remain a solution in NVIDIA’s professional GPU lineup. Quadros are widely used in certified design, CAD, and visualization workflows and offer long-term stability and comprehensive independent software vendor (ISV) certification.

Choosing the right GPU is about picking the right GPU for your specific workload and data.  NVIDIA provides thoughtful examples and descriptions to make GPU selection easier. 

Blackwell is well-suited for agentic AI, fine-tuning, 8K gaming and streaming, hardware accelerated transcoding, physical AI, scientific computing, rendering, 3D graphics, and video applications. Ada Generation GPUs are best for 3D modeling, rendering, video creation, and modern AV1 transcoding workflows. Quadro GPUs are the proven GPU for certified professional graphics environments.

The strengths of these GPUs are even more pronounced when used on Akamai’s distributed cloud to meet the localized and distributed nature of these workloads. Conversational AI interacts with users around the world. Computer vision systems process video where it is generated. Rendering, simulation, and visualization are consumed by globally distributed teams. 

Keeping all these workloads closer to the user improves performance, customer satisfaction, and reduces costs.

A helpful way to choose between the GPU options on Akamai Cloud is to start with how NVIDIA itself describes the workloads that each GPU is designed to accelerate and support.

NVIDIA positions the RTX Pro 6000 Blackwell Server Edition as appropriate for accelerating workloads that span AI inference, fine-tuning, agentic AI, physical AI, scientific computing, rendering, 3D graphics, and video applications. NVIDIA groups these together because this class of GPU is built to handle large inference pipelines, model fine-tuning, 8K AAA title support, and 8K chroma subsampling, along with high-fidelity visual and simulation tasks in production environments.

RTX PRO 6000 Blackwell Server Edition GPUs are available in 1-card, 2-card, and 4-card plans. Learn more.

The RTX 4000 Ada Generation is ideal for 3D modeling, complex rendering, and video content creation and streaming. It’s also great for AI image generation and inference for small language models (SLMs). This reflects its role as a powerful professional workstation GPU for visualization, graphics, and multimedia workflows, with the added benefit of Tensor cores that can accelerate AI inference inside those workflows.

Quadro GPUs have been positioned by NVIDIA for professional graphics, CAD, design visualization, and certified rendering pipelines. Quadro remains synonymous with stable, certified graphics environments used across engineering and design industries.

Using these NVIDIA workload descriptions as a guide makes it easier to map your application to the right GPU (Table).

Comparison chart: Choosing the right GPU for your workload

NVIDIA’s own workload examples for the RTX PRO 6000 Blackwell Server Edition align closely with the types of workloads that thrive on Akamai Cloud’s distributed architecture. These GPU intensive workloads are latency sensitive, data intensive, and often geographically distributed by nature.

Agentic AI systems ingest text, images, and other inputs from users in real time. The system must respond quickly and often operate as part of a live application experience.

NVIDIA highlights agentic AI as a primary workload for Blackwell because of its ability to sustain and scale high throughput inference with large language models (LLMs) and multimodal models that require tens of gigabytes of GPU memory for inference. When these systems run on Akamai’s distributed cloud, inference happens closer to users, reducing latency and accelerating responsiveness for conversational and multimodal interactions across regions.

This is where Blackwell and a distributed cloud align. The GPU on Akamai Cloud handles the scale of the model and the inference load. Akamai delivers proximity to users and global reach.

Physical AI workloads involve high-resolution video streams, cameras, sensors, and continuous visual input from the real world. These inputs are generated at the edge, not in a central data center.

Blackwell GPUs are able to process large visual inputs, run complex vision inference efficiently, and transcode high-resolution video streams in real time, making them ideal for physical AI. When paired with Akamai’s distributed infrastructure, video and sensor data can be processed closer to where it is created instead of being backhauled to a distant region.

Akamai’s distributed cloud reduces bandwidth costs, lowers latency for decision-making, keeps data processed within required geographic boundaries, and enables real-time monitoring and analytics at scale.

NVIDIA groups scientific computing, rendering, 3D graphics, and video applications together because they are all memory-intensive parallel workloads that benefit from large GPU memory and advanced RT and Tensor cores.

These workloads often support distributed teams, remote visualization, digital twins, simulation environments, and video pipelines that must be accessed globally.

Running scientific computing on Blackwell GPUs inside Akamai’s distributed cloud allows high-fidelity graphics, rendering, and simulation outputs to be delivered closer to users, designers, engineers, and operators without requiring centralized GPU farms.