Why GPU Memory Matters More Than You Think

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Contemporary engineering workstations must use hardware and software capable of running the latest engineering applications for ray traced rendering, artificial intelligence (AI) and simulation-enhanced computer-aided engineering (CAE). These applications require compute power that can only be delivered by graphics processing units (GPUs).

All data for these applications must be resident in GPU memory for maximum performance, productivity and creativity. Professional workflows contain large amounts of data which—at some point—typically resides and runs in the memory of a single GPU. This can lead to processing bottlenecks if GPU memory cannot hold the amount of data required. GPU memory capacity must be available to ensure applications take full advantage of GPU-accelerated features and capabilities. Select new GPUs with increased memory capacity can also use technology such as NVIDIA NVLink GPU memory pooling (application support required) to effectively double GPU memory using two GPUs to scale performance along with memory capacity.

Industry Trends Driving Demand for Larger GPU Memory Capacity

AI, rendering, simulation and 4K displays with high dynamic range (HDR) are moving into the mainstream. GPU memory demand is driven by larger models and datasets, multi-application workflows, high-resolution content on multiple displays and collaboration workflows.

Professional workflows are more GPU memory-intensive than ever. There are different GPU memory needs for gamers versus business professionals. Gamers can adjust settings if GPU memory is exceeded      by lowering resolution and cutting textures and light effects. However, there are dire consequences of running out of GPU memory in professional applications and solutions. Professional workloads require available GPU memory because engineers and designers cannot remove design elements or information such as simulation data.

Why Memory is Critical for 3D Design Professionals

Every application consumes GPU compute cycles and GPU memory. Modern multi-application workflows put significant demands on workstations. Insufficient GPU resources can cause applications to revert to slower paths or fail.

There are many reasons why GPU memory is important in modern 3D design:

• Multiple 4K displays require significant GPU memory just to drive the displays
• GPU-accelerated applications and simulation (PhysX) models rely on data being in GPU memory for maximum performance
• The growth of model sizes and the overall complexity of materials and photorealistic models means more GPU memory is required
• AI-enhanced applications require trained GPU-accelerated deep neural networks (DNNs)—often more than one—to be available in GPU memory

GPU performance and GPU memory are directly related. For best performance, data needs to be in GPU memory. This occurs because system memory provides slower data transfer to a GPU while GPU memory provides fast data transfer to a GPU.

Greater functionality is contingent on GPU memory availability. Larger GPU memory allows users to run more applications simultaneously, use more plug-ins and tools, run higher fidelity calculations and work with higher-resolution models and images.

Solving GPU Memory Issues

Cutting-edge professional GPUs are equipped with more memory than ever before. For example, the NVIDIA RTX desktop workstation line (RTX A6000, A5500, A5000, A4500, A4000 and A2000 12GB) delivers up to 24 GB of graphics memory and selectable error correction code (ECC) to supercharge rendering, Al, graphics, simulation and compute tasks.

The NVIDIA RTX A4500 and NVIDIA RTX A5500 cards, the latest professional solutions based on NVIDIA’s Ampere GPU architecture, provide the performance and capabilities required for demanding multi-application workflows with additional GPU memory headroom. The NVIDIA RTX A4500 provides 4 GB more GPU memory than the RTX A4000 along with NVLink support, doubling GPU memory capacity to 40 GB, while the RTX A5500 delivers 24 GB of GPU memory and also supports NVLink, effectively doubling capacity to 48 GB when using NVLink-aware applications. 

Doubling GPU Memory

With an increased need for high GPU memory, some technology vendors are providing unique options to increase capacity. For instance, NVIDIA’s NVLink interconnect technology allows two GPUs to communicate directly via a high-speed bridge at speeds typically twice that of PCIe within workstations or servers. It allows the available memory of multiple GPUs to be combined and accessible (pooled) at all times (application support required).

NVLink technology delivers up to 112.5 gigabytes per second of bidirectional bandwidth, and with a combined graphics memory capacity of up to 96 GB (NVIDIA RTX A6000) it can help tackle the largest imaging processing, virtual reality, or AI training datasets and inferencing tasks. Combining two NVIDIA RTX A4500 or NVIDIA RTX A5500 graphic cards with NVIDIA NVLink can provide performance improvements of 20 – 30 percent.

Essential Business Metrics for Engineering and 3D Workstations

Many engineering tools now require additional processing performance and capabilities that only GPUs with large amounts of GPU memory can provide. Modern GPUs need to be part of a scalable accelerated computing workflow capable of handling every workload for individuals or geographically distributed teams.

Modern professional workflows, AI models, display data and high-resolution displays (particularly with HDR) require larger GPU memory capacity. Sufficient GPU memory is important because it increases performance, functionality and creativity, all of which directly leads to improved productivity.

It is important to consider the following business metrics when selecting an engineering or 3D design workstation:

• Cost of ownership: Does it provide a strong return on investment (ROI) and predictable future costs?
• Flexibility and scalability: Does it support a variety of compute, graphics and AI-intensive workloads?
• GPU memory: Is there adequate available GPU memory to provide the performance needed for professional, AI, and simulation workloads both now and into the future?

Using GPUs with increased memory capacity lets users work faster at higher quality using the latest technologies and tools. In addition, these tools let users do their best work now and future proof infrastructure investment for the challenges of tomorrow.

These and other topics will be covered in considerable detail during an upcoming PNY/NVIDIA webinar: Groundbreaking Innovation is More Accessible Than Ever – NVIDIA RTX Hardware Update. The webinar will take place on August 25, 2022 at 12:00pm EDT (9:00am PDT). All live webinar attendees will be entered into a drawing for a chance to win an NVIDIA RTX A4500.