GPU laptop built for deep learning

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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.

I/O port overview

Kensington lock slot

MicroSD

3.5 mm headphone + mic

USB 3 (powered)

3.5mm mic + S/PDIF

USB 3

HDMI

Thunderbolt 3

Gigabit ethernet (RJ45)

Technical specifications

GPU

RTX 2080 Super Max-Q

8 GB GDDR6, 3072 CUDA cores, 1080 MHz base clock

Processor

8 cores, 2.30 GHz

Intel Core i7-10875H with 16 threads, 5.10 GHz turbo, and 16 MB cache

Memory

64 GB

of 2666 MHz DDR4 SO-DIMM

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 powered (AC/DC) USB 3.2 Gen 1 Type A

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