GPU laptop built for deep learning

Powered by the NVIDIA RTX 2080 Super Max-Q GPU. Pre-installed with TensorFlow, PyTorch, Keras, CUDA, and cuDNN and more.

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1000+ research groups trust Lambda

Easy system administration

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

Ubuntu 20.04
Keras
TensorFlow
PyTorch
Caffe
Theano
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 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

Frequently asked questions

Not seeing the answer to your question? Contact sales

What if my laptop develops a hardware issue?
Lambda offers perpetual support from our engineers and up to 3 years of warranty. If you experience issues with your TensorBook, our engineers are available by email at support@lambdalabs.com and by phone at +1 (855) 882-6011. We will immediately replace any faulty components (e.g. GPU, CPU, battery), which you may return at your convenience. If your TensorBook is still under warranty, all replacements will be free of charge. Solutions to technical issues may also be found within our community, Deep Talk.
Why is there no 4K (Ultra HD) display option?
In short: battery life. Rendering a 4K display is extremely costly from a power perspective. For most people a longer battery life is more important than a higher resolution display.
Are discounts available for academics or students?
Yes, we offer a 3% discount on TensorBooks for academics and students. To get your discount, email us at enterprise@lambdalabs.com or by phone at +1 (866) 711-2025.
Are discounts available for bulk purchases?
Yes, we offer bulk discounts. Pricing will depend on project size. For details, please contact us by email at enterprise@lambdalabs.com or by phone at +1 (866) 711-2025.
How long will it take for my laptop to ship?
Your TensorBook will typically ship within 1 to 3 business days after purchase.
Does Lambda offer shipping to countries outside the United States?
Yes, Lambda ships globally. Please note if you're outside the United States, your order may be subject to duties and tariffs. For more information, please contact our sales team.
What operating systems are available for the TensorBook?
The TensorBook can be pre-installed with either Ubuntu 18.04, Ubuntu 16.04, or Windows 10 Pro.
Does Lambda offer dual booting of operating system?
Yes, we can dual boot your TensorBook with Windows 10 Pro and Ubuntu 18.04. For more details, please contact us by email at enterprise@lambdalabs.com or by phone at +1 (866) 711-2025.
What payment methods are accepted?
We accept wire transfers, ACH transfers, and credit cards. Please note that credit card transactions are subject to an additional 3% charge.
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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