Select your [[ build.model.nick ]] model

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

[[ build.model.image_alt ]]

Select your [[ build.model.nick ]] model

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

Basic

OS Ubuntu 16.04
GPUs 4x NVIDIA 1080 TI
CPU Intel Core i7-6850K
RAM 64 GB DDR4 RAM
STORAGE 2 TB SATA SSD (OS install)
EXTRA 2 TB HDD
NETWORK 1 Gbps ethernet

Premium

OS Ubuntu 16.04
GPUs 4x NVIDIA 1080 Ti
CPU Intel Core i7-6850K
RAM 128 GB DDR4 RAM
STORAGE 2 TB SATA SSD (OS install)
EXTRA 4 TB RAID 5 array (3x HDDs)
NETWORK 10 Gbps ethernet

Max

OS Ubuntu 16.04
GPUs 4x NVIDIA Titan V
CPU Intel Xeon E5-2650 v4
RAM 128 GB DDR4 RAM
STORAGE 2 TB SATA SSD (OS install)
EXTRA 4 TB RAID 5 array (3x HDDs)
NETWORK 10 Gbps ethernet

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Who bought a Basic?

About the Basic

GPUs

GPUs are the most critical piece of hardware for Deep Learning. The Basic has 4x NVIDIA GTX 1080 Ti GPUs (Pascal Architecture). For Deep Learning in 2018, the 1080 Ti offers the best price/performance trade-off of any GPU on the market. Each 1080 Ti has 11.3 TFLOPs of FP32 performance (the standard precision for Deep Learning training). For most tasks, the 1080 Ti is 95% as fast as the NVIDIA Titan Xp and 70% as fast as the NVIDIA Titan V.

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 Intel's i7-6850K (6 cores, 40x PCI-e lanes). Its core-to-GPU ratio is 1.5, which follows the best practice of at least 1 CPU core per GPU. The Basic's CPU, combined with its PLX-enabled motherboard, provides 16x PCI-e lanes to each GPU (the max possible).

Motherboard

A motherboard's PCI-e lanes have a significant effect on Deep Learning training performance. PCI-e lanes are data pipes connecting the GPUs and CPU. The number of PCI-e lanes connecting each GPU to the CPU varies from 4x to 16x, depending on the motherboard. More PCI-e lanes is better because more lanes = more throughput. The Basic's motherboard has PLX chips, which ensures that each GPU gets 16x PCI-e lanes (the maximum possible as of 2018).

RAM

A Deep Learning computer should have at least as much RAM as GPU memory. For example, a machine with 2x NVIDIA 1080 Ti GPUs should have at least 22 GB of memory (1080 Tis have 11 GB of memory each). The Basic has 4x 1080 Ti GPUs and 64 GB of DDR4 2400 MHz memory, so it follows this rule of thumb. If you work with large data sets (e.g. many large images), consider upgrading to Premium, which has 128 GB of memory.

Storage

A large data set will not completely fit into RAM; some data must remain on storage. During training, data will be repeatedly loaded from storage to RAM. If the storage is slow, the GPUs will waste cycles waiting for data. The Basic has two storage devices: a 2 TB SSD (fast) and a 2 TB HDD (slower). This way, you can keep the current training data on the SSD and move the rest to the HDD. When you're ready to train on different data, just move it to the SSD.

Network

Network interface speed is irrelevant to Deep Learning performance, unless you're doing multi-node distributed training (in which case, you'll want at least 40 Gbps). With 1 Gbps ethernet, this desktop's network interface is far faster than most ISPs provide. If you frequently copy large files between computers, you may consider upgrading to Premium, which has 10 Gbps.

Who bought a Premium?

About the Premium

GPUs

GPUs are the most critical piece of hardware for Deep Learning. The Premium has 4x NVIDIA GTX 1080 Ti GPUs (Pascal Architecture). For Deep Learning in 2018, the 1080 Ti offers the best price/performance trade-off of any GPU on the market. Each 1080 Ti has 11.3 TFLOPs of FP32 performance (the standard precision for Deep Learning training). For most tasks, the 1080 Ti is 95% as fast as the NVIDIA Titan Xp and 70% as fast as the NVIDIA Titan V.

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 Intel's i7-6850K (6 cores, 40x PCI-e lanes). Its core-to-GPU ratio is 1.5, which follows the best practice of at least 1 CPU core per GPU. The Premium's CPU, combined with its PLX-enabled motherboard, provides 16x PCI-e lanes to each GPU (the max possible).

Motherboard

A motherboard's PCI-e lanes have a significant effect on Deep Learning training performance. PCI-e lanes are data pipes connecting the GPUs and CPU. The number of PCI-e lanes connecting each GPU to the CPU varies from 4x to 16x, depending on the motherboard. More PCI-e lanes is better because more lanes = more throughput. The Premium's motherboard has PLX chips, which ensures that each GPU gets 16x PCI-e lanes (the maximum possible as of 2018).

RAM

A Deep Learning computer should have at least as much RAM as GPU memory. For example, a machine with 2x NVIDIA 1080 Ti GPUs should have at least 22 GB of memory (1080 Tis have 11 GB of memory each). The Premium has 4x 1080 Ti GPUs and 128 GB of DDR4 2400 MHz memory, so it follows this rule of thumb. If you work with large data sets (e.g. many large images), a workstation with 128 GB of memory is standard.

Storage

A large data set will not completely fit into RAM; some data must remain on storage. During training, data will be repeatedly loaded from storage to RAM. If the storage is slow, the GPUs will waste cycles waiting for data. The Premium has two storage devices: a 2 TB SSD on which the OS is installed and a 4 TB RAID 5 array (3x 2 TB HDDs). RAID 5 provides an excellent trade-off between performance and data security. Specifically, it provides 2x the read speed of an individual HDD and has a 1-drive failure fault tolerance.

Network

Network interface speed is irrelevant to Deep Learning performance, unless you're doing multi-node distributed training (in which case, you'll want at least 40 Gbps). With 10 Gbps ethernet, this desktop's network interface is faster than virtually every ISP. If you frequently copy large files between computers, the Premium's 10 Gbps is an excellent feature.

Who bought a Max?

About the Max

GPUs

GPUs are the most critical piece of hardware for Deep Learning. The Max has 4x NVIDIA Titan V GPUs (Volta Architecture). The Titan V is powered by the same chip as the NVIDIA Tesla V100. Each Titan V has 13.8 TFLOPs of FP32 performance, the standard precision for Deep Learning training. For most tasks, the Titan V is about 42% faster than the 1080 Ti and 40% faster than the Titan Xp.

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 Max uses an Intel Xeon E5-2650 v4 (12 cores, 40x PCI-e lanes). Its core-to-GPU ratio is 3, which follows the best practice of at least 1 CPU core per GPU. The Max's CPU, combined with its PLX-enabled motherboard, provides 16x PCI-e lanes to each GPU (the max possible).

Motherboard

A motherboard's PCI-e lanes have a significant effect on Deep Learning training performance. PCI-e lanes are data pipes connecting the GPUs and CPU. The number of PCI-e lanes connecting each GPU to the CPU varies from 4x to 16x, depending on the motherboard. More PCI-e lanes is better because more lanes = more throughput. The Max's motherboard has PLX chips, which ensures that each GPU gets 16x PCI-e lanes (the maximum possible as of 2018).

RAM

A Deep Learning computer should have at least as much RAM as GPU memory. For example, a machine with 2x NVIDIA Titan V GPUs should have at least 24 GB of memory (Titan Vs have 12 GB of memory each). The Max has 4x NVIDIA Titan V GPUs and 128 GB of DDR4 2400 MHz memory, so it follows this rule of thumb. If you work with large data sets (e.g. many large images), a workstation with 128 GB of memory is standard.

Storage

A large data set will not completely fit into RAM; some data must remain on storage. During training, data will be repeatedly loaded from storage to RAM. If the storage is slow, the GPUs will waste cycles waiting for data. The Max has two storage devices: a 2 TB SSD on which the OS is installed and a 4 TB RAID 5 array (3x 2 TB HDDs). RAID 5 provides an excellent trade-off between performance and data security. Specifically, it provides 2x the read speed of an individual HDD and has a 1-drive failure fault tolerance.

Network

Network interface speed is irrelevant to Deep Learning performance, unless you're doing multi-node distributed training (in which case, you'll want at least 40 Gbps). With 10 Gbps ethernet, this desktop's network interface is faster than virtually every ISP. If you frequently copy large files between computers, the Max's 10 Gbps is an excellent feature.

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