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GPU laptop built for deep learning

Powered by the NVIDIA RTX 3080 Max-Q GPU. Pre-installed with TensorFlow, PyTorch, CUDA, cuDNN and more.

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5000+ research groups trust Lambda

Easy system administration

Our deep learning laptop comes with Lambda Stack, which includes frameworks like TensorFlow and PyTorch. Lambda Stack makes upgrading these frameworks easy.

Ubuntu 20.04
TensorFlow
PyTorch
Keras
NVIDIA CUDA
NVIDIA cuDNN

I/O port overview

Kensington lock slot
MicroSD
3.5 mm headphone + mic
USB 3 (powered)
3.5mm mic + S/PDIF
USB 3
USB 3
HDMI
Thunderbolt 3
Gigabit ethernet (RJ45)

Technical specifications

GPU
RTX 3080 Max-Q
16 GB GDDR6, 6144 CUDA cores, 1245-1710 MHz boost clock
Processor
8 cores, 2.20 GHz
Intel Core i7-10870H with 16 threads, 5.00 GHz turbo, and 16 MB cache
Memory
64 GB
of 2666 MHz DDR4 SDRAM
Storage
2 TB
Samsung 970 EVO with 1 TB, 3,500 MB/s seq. read, 2,500 MB/s seq. write
Samsung 860 EVO with 1 TB, 550 MB/s seq. read, 520 MB/s seq. write
Size & weight
Width: 14.07" (357 mm)
Height: 0.78" (20 mm)
Depth: 9.37" (238 mm)
Weight: 4.39 lbs (2 kgs)
Display
15.6"
1920x1080 (Full HD), 144 Hz, Matte, 72% NTSC
Audio ports
1x 3.5mm 2-in-1 audio jack (headphone + microphone)
1x 3.5mm 2-in-1 audio jack (microphone + S/PDIF optical)
Video ports
1x HDMI with HDCP
1x Mini DisplayPort 1.4
1x Thunderbolt 3
USB ports
2x USB 3.2 Gen 1 Type A
1x USB 3.2 Gen 1 Type A powered (AC/DC)
Networking
Ethernet: 1x 10/100/1000 Mbps RJ45 port
Wireless: Intel Dual Band WiFi 6 AX
Bluetooth: Supported
Power
Battery: 73 watt-hour embedded 3-cell polymer battery pack
Power adapter: AC in, DC out. Output: 180 watts, 19.5 volts, 9.23 amps
Power input requirements: 100 to 240 VAC at 50 to 60 Hz

Explore our research

Lambda's research papers have been accepted into the top machine learning and graphics conferences, including ICCV, SIGGRAPH Asia, NeurIPS, and ACM Transactions on Graphics (TOG).

HoloGAN: Unsupervised Learning of 3D Representations from Natural Images
We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images. Learn More
RenderNet: A Deep Conv. Network for Differentiable Rendering from 3D Shapes
Traditional computer graphics rendering pipelines are designed for procedurally generating 2D images from 3D shapes with high performance. Learn More
Adversarial Monte Carlo Denoising with Conditioned Aux. Feature Modulation
Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Learn More