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NVIDIA GeForce RTX Laptops for STEM Majors

NVIDIA GeForce RTX^™ laptops do so much more than boost high-performance gaming applications—they also supercharge studies for STEM majors. Powerful Graphics Processing Units (GPUs) help students work faster, with acceleration for top student applications in engineering, computer science, data science, and economics. NVIDIA Broadcast uses AI to improve audio and video quality, making it easier to engage in lectures delivered online and to collaborate with lab group members from anywhere. And now, NVIDIA’s world-leading AI software stack comes to Windows 11, helping students studying artificial intelligence and data science learn on the same tools that they will use in the industry.

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Study Faster With GPU Acceleration

GeForce RTX laptops provide GPU acceleration for dozens of STEM tools and applications, allowing you to work faster and complete coursework sooner

Engineering

More interactive, realtime rendering for 3D design and modeling. Faster solutions and visualization for mechanical, structural, and electrical simulations.

Computer Science, Data science, Economics

Faster data analytics for processing large data sets. Faster training for deep learning and traditional machine learning models for computer vision, natural language processing, and tabular data.

With GeForce RTX laptops, you’ll work faster, giving you more time to explore the topics that interest you.

Top STEM Software Applications Accelerated By GeForce Laptops

STEM Application Performance

Engineering

RTX 3080 Ti Laptop GPU

RTX 3050 Ti Laptop GPU

Average Laptop

SOLIDWORKS
Up to 8x Faster

Enscape

Matlab
Up to 4x faster

SOLIDWORKS: GRCAD Black Owl Test at 1080p Revit + Enscape Video path on Enscape Revit sample project, ray tracing disabled MATLAB Geomean of single-precision tests CPU/iGPU is Intel Core Core i9 12th Gen

Computer, Data Science, and Economics

(Lower is Better)

RTX 3080 Ti Laptop GPU

RTX 3060 Laptop GPU

Average Laptop

TensorFlow/Resnet50 Training

RAPIDS

TensorFlow training is MLPerf-compliant ResNet50 running in WSL, single epoch training time calculated from measured images/sec.

RAPIDS is decomposition with 50k/100k samples. CPU is Intel Core Core i9 12th Gen.

GeForce Laptops help design, model, and simulate components, systems, and structures

Design, Model, Simulate

Planning on studying engineering or architecture? GPUs have become fundamental tools for designing, modeling, and simulating components, systems, and structures. Take on larger and more challenging 3D class design projects with smooth, interactive rendering in SOLIDWORKS, complete complex simulations faster with MATLAB and ANSYS Discovery, and visualize your architectural masterpieces in real time with Enscape. With GeForce RTX laptops, you know you have the right tools for the job.

NVIDIA’s GPU-accelerated AI and data science software stack

Run World-leading Artificial Intelligence Software

Interested in artificial intelligence or data science? Learn the same technology that you will use in industry with NVIDIA’s GPU-accelerated AI and data science software stack. Use cuDNN with GPU acceleration to train neural networks with TensorFlow, PyTorch, WinML, or MxNet. Run NVIDIA RAPIDS for data science with cuDF, cuML, and cuGraph (GPU-accelerated alternatives to Pandas, SciKit-Learn, and NetworkX). NVIDIA software helps you train models faster and process more samples to get better accuracy and more insightful results. And, thanks to CUDA on Windows Subsystem for Linux (WSL2), it’s now available on Windows 11. This capability allows Windows users to run Linux applications simultaneously—a true multi-OS environment for future data scientists.

NVIDIA’s AI software stack is designed for Linux and is currently being tested for WSL 2. WSL is currently available as part of Microsoft’s Windows Insider Program

GeForce RTX Laptops are the Best Computers for STEM Students

No longer do students have to choose between a computer for school or a computer for fun. With GeForce RTX laptops you can study, create, and play—all on the same platform.

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