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Choose the price, not the parts. Each model is built with the GPUs, CPU, RAM, and storage that maximizes Deep Learning performance per dollar.

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Basic

OS Ubuntu 18.04
Display 15.6in display (3840x2160)
GPU NVIDIA GTX 1070
CPU Intel i7-8750H
Memory 16 GB memory
STORAGE 500 GB NVMe SSD
Protection 1 year warranty

Premium

OS Ubuntu 18.04
Display 15.6in display (3840x2160)
GPU NVIDIA GTX 1070
CPU Intel i7-8750H
Memory 32 GB memory
STORAGE 500 GB NVMe SSD
Protection 1 year warranty

Max

OS Ubuntu 18.04
Display 15.6in display (3840x2160)
GPU NVIDIA GTX 1070
CPU Intel i7-8750H
Memory 32 GB memory
STORAGE 1 TB NVMe SSD
Protection 1 year warranty

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About the Basic

GPU

GPUs are the most critical piece of hardware for Deep Learning. The Basic has an NVIDIA GTX 1070 GPU (Pascal Architecture). For Deep Learning in 2018, the 1070 offers the best price/performance trade-off of any laptop GPU. Each 1070 has 8 GB of GDDR5 memory, and 6.5 TFLOPs of FP32 performance: the standard precision for Deep Learning training.

Processor

During training, the CPU preprocesses data and feeds it to the GPUs. A slow processor will cause the GPUs to waste cycles waiting for this data. Core count and PCI-e lane count are important CPU performance factors. More cores means faster data preprocessing; more PCI-e lanes means faster transmission of that data to the GPUs. The Basic uses the Intel i7-8750H (6 cores, 16x PCI-e lanes). Its core-to-GPU ratio is 6, which follows the best practice of at least 1 CPU core per GPU. The Basic's CPU provides 8x PCI-e lanes to the GPU (the maximum of any laptop in 2018).

Memory

Best practice dictates that a Deep Learning computer's RAM-to-GPU-memory ratio be at least 2:1. This laptop's ratio is 2:1: it has 16 GB of RAM and its GPU has 8 GB of memory. If you plan to train models on large datasets (e.g. image-based), consider upgrading to the Max model, which as 32 GB of memory.

Storage

Many datasets do not completely fit into RAM; in such cases, during training, the dataset must be repeatedly partially loaded from storage to RAM. If storage I/O is too slow, the GPUs waste cycles waiting for input. The Basic's storage device is a 500 GB NVMe SSD. NVMe SSDs are the highest performing storage devices on the market as of 2018. They are approximately 6x faster at sequential read tasks and 3.5x faster at sequential write tasks than their SATA SSD counterparts. In short, this laptop's storage will never bottleneck your training. If you work with large datasets (e.g. image-based, like ImageNet), consider upgrading to Max, which has 1 TB.

Network

Network interface speed is not relevant to Deep Learning performance, unless you're doing multi-node distributed training (in which case, you'll want at least 40 Gbps and definitely won't be using a laptop!). With 1 Gbps ethernet, this notebook's network interface is far faster than most ISPs provide.

Display

The Basic has a 15.6 inch display with 3840x2160 resolution (QFHD). It has one of the most colorful, detailed displays of any laptop on the market.

Who bought a Basic?

About the Premium

GPU

GPUs are the most critical piece of hardware for Deep Learning. The Premium has an NVIDIA GTX 1070 GPU (Pascal Architecture). For Deep Learning in 2018, the 1070 offers the best price/performance trade-off of any laptop GPU. Each 1070 has 8 GB of GDDR5 memory, and 6.5 TFLOPs of FP32 performance: the standard precision for Deep Learning training.

Processor

During training, the CPU preprocesses data and feeds it to the GPUs. A slow processor will cause the GPUs to waste cycles waiting for this data. Core count and PCI-e lane count are important CPU performance factors. More cores means faster data preprocessing; more PCI-e lanes means faster transmission of that data to the GPUs. The Premium uses the Intel i7-8750H (6 cores, 16x PCI-e lanes). Its core-to-GPU ratio is 6, which follows the best practice of at least 1 CPU core per GPU. The Premium's CPU provides 8x PCI-e lanes to the GPU (the maximum of any laptop in 2018).

Memory

Best practice dictates that a Deep Learning computer's RAM-to-GPU-memory ratio be at least 2:1. This laptop's ratio is 4:1: it has 32 GB of RAM and its GPU has 8 GB of memory.

Storage

Many datasets do not completely fit into RAM; in such cases, during training, the dataset must be repeatedly partially loaded from storage to RAM. If storage I/O is too slow, the GPUs waste cycles waiting for input. The Premium's storage device is a 500 GB NVMe SSD. NVMe SSDs are the highest performing storage devices on the market as of 2018. They are approximately 6x faster at sequential read tasks and 3.5x faster at sequential write tasks than their SATA SSD counterparts. In short, this laptop's storage will never bottleneck your training. If you work with large datasets (e.g. image-based, like ImageNet), consider upgrading to Max, which has 1 TB.

Network

Network interface speed is not relevant to Deep Learning performance, unless you're doing multi-node distributed training (in which case, you'll want at least 40 Gbps and definitely won't be using a laptop!). With 1 Gbps ethernet, this notebook's network interface is far faster than most ISPs provide.

Display

The Premium has a 15.6 inch display with 3840x2160 resolution (QFHD). It has one of the most colorful, detailed displays of any laptop on the market.

Who bought a Premium?

About the Max

GPU

GPUs are the most critical piece of hardware for Deep Learning. The Premium has an NVIDIA GTX 1070 GPU (Pascal Architecture). For Deep Learning in 2018, the 1070 offers the best price/performance trade-off of any laptop GPU. Each 1070 has 8 GB of GDDR5 memory, and 6.5 TFLOPs of FP32 performance: the standard precision for Deep Learning training.

Processor

During training, the CPU preprocesses data and feeds it to the GPUs. A slow processor will cause the GPUs to waste cycles waiting for this data. Core count and PCI-e lane count are important CPU performance factors. More cores means faster data preprocessing; more PCI-e lanes means faster transmission of that data to the GPUs. The Premium uses the Intel i7-8750H (6 cores, 16x PCI-e lanes). Its core-to-GPU ratio is 6, which follows the best practice of at least 1 CPU core per GPU. The Premium's CPU provides 8x PCI-e lanes to the GPU (the maximum of any laptop in 2018).

Memory

Best practice dictates that a Deep Learning computer's RAM-to-GPU-memory ratio be at least 2:1. This laptop's ratio is 4:1: it has 32 GB of RAM and its GPU has 8 GB of memory. This extra RAM provides improved performance for training neural nets on large amounts of data (e.g. image-based datasets).

Storage

Many datasets do not completely fit into RAM; in such cases, during training, the dataset must be repeatedly partially loaded from storage to RAM. If storage I/O is too slow, the GPUs waste cycles waiting for input. The Max's storage device is a 1 TB NVMe SSD. NVMe SSDs are the highest performing storage devices on the market as of 2018. They are approximately 6x faster at sequential read tasks and 3.5x faster at sequential write tasks than their SATA SSD counterparts. In short, this laptop's storage will never bottleneck your training. If you work with large datasets like ImageNet, the Max's 1 TB of storage is a must.

Network

Network interface speed is not relevant to Deep Learning performance, unless you're doing multi-node distributed training (in which case, you'll want at least 40 Gbps and definitely won't be using a laptop!). With 1 Gbps ethernet, this notebook's network interface is far faster than most ISPs provide.

Display

The Max has a 15.6 inch display with 3840x2160 resolution (QFHD). It has one of the most colorful, detailed displays of any laptop on the market.

Who bought a Max?

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