www.engineering.com
Open in
urlscan Pro
34.95.33.68
Public Scan
URL:
https://www.engineering.com/story/why-gpu-memory-matters-more-than-you-think
Submission: On August 18 via api from US — Scanned from CA
Submission: On August 18 via api from US — Scanned from CA
Form analysis 1 forms found in the DOM
<form class="SignupPopupBlockWrapper__SignupBlockForm-sc-1353rsc-3 cQThwv">
<div class="SignupPopupBlockWrapper__FieldsWrapper-sc-1353rsc-0 fHNvMh">
<div class="SignupPopupBlockWrapper__GenericFieldWrapper-sc-1353rsc-4 ivThAQ">
<div class="styles__FieldRow-nn7pvt-3 iRAaIM wisepops-field wisepops-field-email"><input class="styles__BasicInput-nn7pvt-0-input VNgiu wisepops-field wisepops-field-email" name="email" required="" type="email"
pattern="^[a-zA-Z0-9.!#$%&’*+/=?^_`{|}~-]+@[a-zA-Z0-9-]+(?:\.[a-zA-Z0-9-]+)+$" placeholder="email@website.com" title="john.doe@example.com"></div>
</div><input type="hidden" name="sectionname" value="Workstations"><label for="wisepops-signup-disclaimer" color="#444" font-family="Roboto" font-size="12" font-style="normal" font-weight="400" text-decoration="none"
class="disclaimer__Label-x3ofxw-0 bipQYp"><input required="" id="wisepops-signup-disclaimer" name="accept_disclaimer" type="checkbox" class="disclaimer__Checkbox-x3ofxw-1 iaboGY">I agree to receive occasional email news updates</label><button
class="SignupPopupSubmitButton__InnerSubmitButton-sc-1jovqh8-0 giMpjb">Subscribe</button>
</div>
</form>Text Content
* * My Projects
* Topics
* Resources
* TV
* Communities
*
*
* Log in
* HOME
WHY GPU MEMORY MATTERS MORE THAN YOU THINK
Applications are asking more of graphics cards, and memory can be a big
bottleneck—if you don’t plan ahead.
WRITTEN BY
Sponsor
PUBLISHED
Aug 11, 2022
READING TIME
~5 mins
LISTEN TO STORY
0
952
1
Share
PNY Technologies has submitted this post.
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.
Manufacturing use cases made possible with higher GPU memory. (Image courtesy of
PNY.)
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.
The NVIDIA RTX A5500 and NVIDIA RTX A4500 graphics cards. (Image courtesy of
PNY.)
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.
Two NVIDIA RTX A4500s with NVLink connector. (Image courtesy of PNY.)
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.
For more information on choosing the right GPU for CAD engineering or 3D design
workstations, visit PNY.com.
LEARN MORE AT PNY
Likes
Reply
Share
Facebook
Twitter
Linkedin
--------------------------------------------------------------------------------
Mail
Link
Share your thoughts Reply
Downloading File
0 0% Complete
WRITTEN BY
Sponsor Follow
RELATED TOPICS
HARDWARE Workstations
SEE MORE LIKE THIS
NVIDIA’s Newest GPU Extends VR Senses An Entry-Level Workstation That’s Far More
Powerful Than “Entry Level” What’s the Difference Between GeForce and Quadro
Graphics Cards? How NVIDIA Quadro RTX Makes Virtual Reality More Realistic
Configuring Your Workstation for Design Visualization
Personalize Feed
Curate your engineering.com homepage. See the stories you care about first.
* Powered by:
* * About About
* Contact Contact
* Terms Terms
* FAQ
* Advertise with us Advertise
* *
*
*
* Deutsch de
|
* Copyright 2022 engineering.com
Workstations News For Engineers
SIGN UP TODAY TO GET WEEKLY UPDATES ON STORIES, WEBINARS, RESEARCH AND MORE.
I agree to receive occasional email news updatesSubscribe