TensorBook

Deep Learning Laptop

GPU laptop with RTX 2070 Max-Q or RTX 2080 Max-Q. Ubuntu, TensorFlow, PyTorch, Keras, CUDA, and cuDNN pre-installed.

TensorBook, a GPU Laptop for Deep Learning

Trusted by thousands of customers worldwide

Researchers and engineers at universities, start-ups, Fortune 500s, public agencies, and national labs use Lambda to power their artificial intelligence workloads.

Apple is a customer MIT is a customer Los Alamos National Lab is a customer Carnegie Mellon University is a customer Google is a customer Wells Fargo is a customer Anthem is a customer

Recommended Designs

Optimized configurations that won't bottleneck

Our recommended designs are benchmarked and tuned to eliminate CPU, memory, and storage bottlenecks when running deep learning workloads.

Basic
Tensorbook Basic, a Deep Learning Laptop with RTX 2070 Max-Q

RTX 2070 Max-Q

In Stock
Intel i7-9750H Processor (6 Cores)
32 GB DDR4 Memory
1 TB SSD (NVMe)
16.1" Display (1920x1080)
$ 2,600
Customize
Premium
Tensorbook Premium, a Deep Learning Laptop with RTX 2070 Max-Q

RTX 2080 Max-Q

In Stock
Intel i7-9750H Processor (6 Cores)
32 GB DDR4 Memory
1 TB SSD (NVMe)
16.1" Display (1920x1080)
$ 3,100
Customize
Max
Tensorbook Max, a Deep Learning Laptop with RTX 2080 Max-Q

RTX 2080 Max-Q

In Stock
Intel i7-9750H Processor (6 Cores)
64 GB DDR4 Memory
1 TB SSD (NVMe)
16.1" Display (1920x1080)
$ 3,300
Customize
Custom

Custom Laptop

In Stock
+1 (866) 711-2025
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Fully Customizable

Not seeing what you're looking for?

GPUs, processors, storage, networking, and memory are fully customizable. Get in touch and we'll design a system that matches your specifications.

Zero setup, easy updates

Lambda Stack comes free with your computer. Machine Learning libraries work out-of-the box and can be updated automatically.

Ubuntu 18.04 comes pre-installed
TensorFlow comes pre-installed
PyTorch comes pre-installed
Keras comes pre-installed

Explore Lambda's Research

Our 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