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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 + Lambda Stack
GPUs 4x NVIDIA RTX 2080
CPU Intel E5-1650 v4
Memory 64 GB memory
STORAGE 2 TB SSD (OS install)
EXTRA 4 TB HDD

Premium

OS Ubuntu + Lambda Stack
GPUs 4x NVIDIA RTX 2080 Ti
CPU Intel Core i9-7920X
Memory 128 GB memory
STORAGE 2 TB SSD (OS install)
EXTRA 4 TB HDD

Max

OS Ubuntu + Lambda Stack
GPUs 4x NVIDIA RTX 2080 Ti
CPU Intel Core i9-7920X
Memory 128 GB memory
STORAGE 4 TB SSD (OS install)
EXTRA 4 TB HDD
NETWORK 10 Gbps ethernet

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About the [[ build.model.title ]] Basic

GPUs

GPUs are the most critical piece of hardware for Deep Learning. The Basic has 4x NVIDIA RTX 2080 GPUs (Turing Architecture) and 8 GB of vRAM. Each RTX 2080 has 10.1 TFLOPs of FP32 performance (the standard precision for Deep Learning training). Our benchmarks show that the 2080 is the same speed as the previous generation GTX 1080 Ti.

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 E5-1650 v4 (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).

Memory

A Deep Learning computer should have at least as much RAM as GPU memory. According to this rule, a machine like the Basic, which has 4x NVIDIA 2080 GPUs, should have at least 32 GB of memory (4 GPUs * 8 GB memory per GPU). The Basic has 64 GB of memory, which provides plenty of overhead. If you work with large data sets (e.g. many large images), consider upgrading to Premium, which has 128 GB of memory.

Storage

Most datasets do not fit in RAM. In such cases, during model training, subsets must be repeatedly swapped in and out of RAM from nonvolatile storage. Such a pipeline requires fast, solid state storage; without it, the GPUs would waste cycles waiting for their next batch of data. The Basic was designed with this constraint in mind; it has two nonvolatile storage devices: a 2 TB solid state drive (fast) for data you're training on now, and a 4 TB hard disk drive (slower) for everything else. Files located in the /data directory are stored on the HDD; all other files are stored on 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 Basic?

About the [[ build.model.title ]] Premium

GPUs

GPUs are the most critical piece of hardware for Deep Learning. The Premium has 4x NVIDIA RTX 2080 Ti GPUs (Turing Architecture). Each RTX 2080 Ti has 13.4 TFLOPs of FP32 performance (the standard precision for Deep Learning training). Our benchmarks show that the 2080 Ti is approximately 30% faster than the previous generation GTX 1080 Ti.

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 Core i9-7920x (12 cores, 44x PCI-e lanes). Its core-to-GPU ratio is 3, 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).

Memory

A Deep Learning computer should have at least as much RAM as GPU memory. According to this rule, a machine like the Premium, which has 4x NVIDIA 2080 Ti GPUs, should have at least 44 GB of memory (4 GPUs * 11 GB memory per GPU). The Premium has 128 GB of memory, which provides plenty of overhead. The 128 GB of memory available on the Premium is quite standard for researchers who work with large data sets (e.g. many large images).

Storage

Most datasets do not fit in RAM. In such cases, during model training, subsets must be repeatedly swapped in and out of RAM from nonvolatile storage. Such a pipeline requires fast, solid state storage; without it, the GPUs would waste cycles waiting for their next batch of data. The Premium was designed with this constraint in mind; it has two nonvolatile storage devices: a 2 TB solid state drive (fast) for data you're training on now, and a 4 TB hard disk drive(slower) for everything else. Files located in the /data directory are stored on the HDD; all other files are stored on the SSD.

Network

Network

Network interface speed is irrelevant to Deep Learning performance, unless you're doing multi-node distributed training (in which case, you'll need at least 40 Gbps), or pulling training data from network attached storage. With 1 Gbps ethernet, this desktop's network interface is far faster than most ISPs provide. If your training data is stored on a remote server on the same LAN, consider upgrading to Max, which has 10 Gbps network speed.

Who bought a Premium?

About the [[ build.model.title ]] Max

GPUs

GPUs are the most critical piece of hardware for Deep Learning. The Max has 4x NVIDIA RTX 2080 Ti GPUs (Turing Architecture). Each RTX 2080 Ti has 13.4 TFLOPs of FP32 performance (the standard precision for Deep Learning training). Our benchmarks show that the 2080 Ti is approximately 30% faster than the previous generation GTX 1080 Ti.

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 Core i9-7920x (12 cores, 44x PCI-e lanes). Its core-to-GPU ratio is 3, 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 Max's motherboard has PLX chips, which ensures that each GPU gets 16x PCI-e lanes (the maximum possible as of 2018).

Memory

A Deep Learning computer should have at least as much RAM as GPU memory. According to this rule, a machine like the Max, which has 4x NVIDIA 2080 Ti GPUs, should have at least 44 GB of memory (4 GPUs * 11 GB memory per GPU). The Max has 128 GB of memory, which provides plenty of overhead. The 128 GB of memory available on the Max is quite standard for researchers who work with large data sets (e.g. many large images).

Storage

Most datasets do not fit in RAM. In such cases, during model training, subsets must be repeatedly swapped in and out of RAM from nonvolatile storage. Such a pipeline requires fast, solid state storage; without it, the GPUs would waste cycles waiting for their next batch of data. The Max was designed with this constraint in mind; it has two nonvolatile storage devices: a massive 4 TB solid state drive (fast) for data you're training on now, and a 4 TB hard disk drive (slower) for everything else. Files located in the /data directory are stored on the HDD; all other files are stored on 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 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.

Who bought a Max?

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