AI Development Box Buyers Guide

What is a Dev Box Pro?

Powered by the latest NVIDIA GPU Accelerators, our Professional Development Boxes are high performance deep learning workstations that enable data scientists to develop and debug models and create a Minimum Viable Product (MVP) with their data sets. Dev Box Pros are built using consumer-grade hardware for maximum value for money and are not intended for datacentre use.

Scan is an Elite Solution Provider for NVIDIA DGX Systems, has dedicated AI support team including data scientists, and has developed a unique range of Dev Box Pros. This page will guide you through what to consider when choosing a deep learning workstation.

Dev Box Pro

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Scan Data Science Workstations can be evaluated online via a Proof of Concept

Book a Test Drive

GPU Accelerator

GeForce RTX 2080 Super

The RTX 2080 Super is the first GPU we recommend. It is based on the latest Turing architecture and has a great combination of CUDA cores, Tensor cores, but with only 8GB of memory is only useful with small datasets.

GeForce RTX 2080 Ti

The next GPU up in the range, the RTX 2080 Ti, has more CUDA cores and Tensor cores, plus expands the memory to 11GB allowing larger datasets to be used.

TITAN RTX

The third GPU, the TITAN RTX, not only has the most CUDA cores and Tensor cores but a far more generous 24GB of memory, enabling the use of larger datasets and higher levels of precision.

The GPU Accelerator is the most important component in a Dev Box Pro as it the main driver for rapid processing and accuracy in your model development and training. We recommend consumer-grade NVIDIA GeForce and TITAN GPU accelerators for maximum value for money. We also have a range of Data Science Workstations that are equipped with NVIDIA Quadro enterprise-grade GPUs. The latest Turing-architecture GeForce RTX GPUs include Tensor cores which are specifically design to accelerate deep learning workloads, while the NVLink bus allows the VRAM on multiple GPUs to appear as a single ultra-fast memory pool to your applications.

The following table highlights the key specifications of the three GeForce and TITAN GPUs we recommend in our Dev Box Pros.

GeForce RTX 2080 Super GeForce RTX 2080 Ti TITAN RTX
Architecture Turing Turing Turing
CUDA Cores 3072 4352 4608
Tensor Cores 384 544 576
RT Cores 48 68 72
Base Clock 1650MHz 1350MHz 1350MHz
Memory 8GB GDDR6 11GB GDDR6 24GB GDDR6
Memory Bandwidth 496GB/s 616GB/s 672GB/s
NVLink Bandwidth 50GB/s 50GB/s 100GB/s

Software Stack

We recommend and pre-install the Ubuntu 18.04 operating system plus a custom software stack built on NVIDIA CUDA-X that includes Docker-CE, Nvidia-Docker2 and GPU-optimised libraries.. Other operating systems are available on request.

Host Processor

The host processor or CPU plays an important role in a Dev Box in the data prep stage. We recommend Intel Core i9 processors in most of our systems as they support lots of cores, run at a high frequency and are relatively affordable compared to Xeon processors. We futher improve these CPUs capabilities for deep learning by partnering them with workstation-grade motherboards. These are equipped with PLX chips, which speed up communication between the GPUs and allow up to four GPUs to be supported in select configurations.

System Memory

While having sufficient VRAM on the GPU accelerator is critically important, system performance will be crippled without adequate optimised system memory. We recommend the same or greater amount of system RAM as VRAM, and arm our workstations with dual-channel and quad-channel memory for optimum performance.

Storage

There’s no point in having the fastest and most powerful GPU accelerators, CPUs and system memory if they are starved for data. We recommend the latest high performance NVMe SSDs in our dev boxes, which with a typical read speed of over 3000MB/sec are approximately 500% faster than a SATA SSD and 1900% faster than a traditional HDD. That said, we recognise that you may need to store old projects and documents on your workstation, and an HDD is ideal for this use as they are very cost effective.

Connectivity

Moving data between different systems can be a time consuming process, so to make the most of the fast data processing capabilities of our dev boxes where possible we use motherboards with integrated 10GbE NICs. 10GbE has the added advantage of being compatible with twisted-pair copper CAT 6/6a or CAT7 cabling with RJ connectors, so in most offices you won’t need to install new cabling, just a new switch. Scan is a partner with Intel and Mellanox, and can provide faster NICs such as 25/50/100GbE on request.

Cooling and Power

GPU accelerators consume a lot of power so Scan’s Dev Boxes are equipped with high-quality 80PLUS Gold power supplies, ensuring a reliable and efficient power source for the system. In addition, the cooling system of each workstation is optimised to ensure consistent results each and every time.

Which Dex Box Pro is right for me?

Pre-configured Dex Box ProsWe have designed and built a range of pre-configured and ready to ship Dev Boxes. Each systems has been optimised to provide the best possible performance in deep learning workflows at different price points.

3XS Pro Development Boxes - GeForce

3XS desktop dev boxes are designed to assist in developing and debugging code with test datasets. Scan recommends the DGX family of products for AI training post the development stage. Please be advised, the NVIDIA EULA states that GeForce GPUs are not to be used in a datacentre environment. For this reason, the dev boxes are only available in a desktop case and not intended for datacentre use. As an NVIDIA Elite Partner, we are qualified to discuss all datacentre enquiries and have Proof of Concept datacentre ready products available.

If you don't see the exact spec you want and would like us to custom build a dev box for you please click HERE.

View:

3XS Dev Box Pro Workstations

View our fixed spec range of 3XS Dev Box pros powered by GeForce RTX GPUs. Alternatively, if you can't see what you're looking for, view our configurator to custom-spec a workstation to meet your expect component requirements.

Configure Systems

What is a Data Science Workstation?

Powered by the latest NVIDIA GPU Accelerators, Data Science Workstations are high performance PCs that enable data scientists to develop and debug models and create a Minimum Viable Product (MVP) with their data sets. Data Science Workstations are built using enterprise-grade hardware for maximum reliability, leading to faster business insights and deployments.

Scan is an Elite Solution Provider for NVIDIA DGX Systems, has a dedicated AI support team including data scientists, and has developed a unique range of Data Science Workstations. This page will guide you through what to consider when choosing a Data Science Workstation.

Data Science Workstation

Try before you buy

Scan Data Science Workstations can be evaluated online via a Proof of Concept

Book a Test Drive

Data Science Workstation buyers guide

NGC Ready Workstations

Scan Data Science Workstations have been validated by NVIDIA as "NGC-Ready" for running NGC software. NGC is the hub for GPU-optimised software for deep learning, machine learning, and HPC that takes care of all the plumbing so data scientists, developers, and researchers can focus on building solutions, gathering insights, and delivering business value.

tensorflow

Accelerate
Time-to-Solution
NGC accelerates productivity with easy-to-deploy, optimised AI frameworks and HPC application containers, so users can focus on building their solutions.

pytorch

Simplify
AI Adoption
NGC lowers the barrier to AI adoption by taking care of the heavy lifting (expertise, time, compute resources) with pre-trained models and workflows with best-in-class accuracy and performance.

rapids

Run Anywhere You Have NVIDIA GPUsRun software from NGC on-prem, in the cloud, or using hybrid and multi-cloud deployments. NGC software can be deployed on bare metal servers or on virtualised environments, maximising utilisation of GPUs, portability, and scalability of applications.

jupyter

Deploy NGC Software with ConfidenceEnterprise-grade support for NGC-Ready systems provides direct access to NVIDIA's experts, minimising risk, and maximising system utilisation and user productivity.

GPU Accelerator

Quadro RTX 6000

The RTX 6000 is the first GPU we recommend for a Data Science Workstation. It is based on the latest Turing architecture and has a great combination of CUDA cores, Tensor cores and a generous 24GB of memory.

Quadro RTX 8000

The next GPU up in the range, the RTX 8000, has the same number of CUDA cores and Tensor cores, but doubles the memory to 48GB, which will enable to you work with much larger datasets than on a RTX 6000.

Quadro GV100

The third GPU, the GV100, is based on the Volta architecture, and while somewhat slower at single precision (FP32) is significantly faster at double precision (FP64) and so may be a better choice for some workloads.

The GPU Accelerator is the most important component in a Data Science Workstation as it is the main driver for rapid processing and accuracy in your model development and training. We recommend enterprise-grade NVIDIA Quadro GPU accelerators, as unlike consumer graphics cards, as they are designed and built for sustained use and so provide maximum reliability. The latest Turing-architecture Quadro GPUs include Tensor cores which are specifically designed to accelerate workloads, while the NVLink bus allows the VRAM on multiple GPUs to appear as a single ultra-fast memory pool to your applications.

The following table highlights the key specifications of the three Quadros GPU we recommend in our Data Science Workstations.

NVIDIA Quadro RTX 6000 NVIDIA Quadro RTX 8000 NVIDIA Quadro GV100
Architecture Turing Turing Volta
CUDA Cores 4608 4608 5120
Tensor Cores 576 576 640
RT Cores 72 72 0
Base Clock 1440MHz 1395MHz 1132MHz
Memory 24GB GDDR6 48GB GDDR6 32GB HBM2
Memory Bandwidth 624GB/s 672GB/s 870GB/s
NVLink Bandwidth 100GB/s 100GB/s 200GB/s

Software Stack

We recommend and pre-install the Ubuntu 18.04 operating system plus a custom software stack built on NVIDIA CUDA-X that includes over 15 GPU-optimised libraries. Other operating systems are available on request.

Host Processor

The host processor or CPU plays an important role in a Data Science Workstation in the data prep stage. We recommend enterprise-grade Intel Xeon processors in our systems as they support ECC Registered memory for maximum reliability. Our single-GPU workstations include a single Xeon-W CPU, which are available with up to 18 cores / 36 threads. Our dual-GPU workstations are powered by a pair of Xeon Scalable processors with a total of up to 48 cores / 96 threads.

System Memory

While having sufficient VRAM on the GPU accelerator is critically important, system performance will be crippled without adequate optimised system memory. As already mentioned our Data Science Workstations feature ECC Registered memory. ECC stands for Error Correcting Code, and means that the memory can detect and fix data corruption on the fly. We recommend 128GB of quad-channel RAM in our single-GPU workstations and 192GB of six-channel RAM for our dual-GPU workstations, although both types of system can support more memory should this be required.

Storage

There’s no point in having the fastest and most powerful GPU accelerators, CPUs and system memory if they are starved for data. We recommend the latest high performance NVMe SSDs in our Data Science Workstations, which with a typical read speed of over 3000MB/sec are approximately 500% faster than a SATA SSD and 1900% faster than a traditional HDD. That said, we recognise that you may need to store old projects and documents on your workstation, and an HDD is ideal for this use as they are very cost effective.

Connectivity

Moving data between different systems can be a time consuming process, so to make the most of the fast data processing capabilities we pre-install 10GbE NICs in our Data Science Workstations. 10GbE has the added advantage of being compatible with twisted-pair copper CAT 6/6a or CAT7 cabling with RJ45 connectors, so in most offices you won’t need to install new cabling, just a new switch. Scan is a partner with Intel and Mellanox, and can provide faster NICs such as 25/50/100GbE on request.

Cooling and Power

GPU accelerators consume a lot of power so Scan’s Data Science Workstations are equipped with high-quality 80PLUS Gold power supplies, ensuring a reliable and efficient power source for the system. In addition, the cooling system of each workstation is optimised to ensure consistent results each and every time.

Which Data Science Workstation is right for me?

Pre-configured Data Science WorkstationsWe have designed and built a range of pre-configured and ready to ship Data Science Workstations. Each systems has been optimised to provide the best possible performance in deep learning and machine learning workflows at different price points.

3XS Data Science Workstations - Quadro

3XS Data Science Workstations are designed to assist in developing and debugging code with test datasets. Scan recommends the DGX family of products for AI training post the development stage. These dev boxes powered by professional-grade NVIDIA Quadro GPU-accelerators and Intel Xeon CPUs equiped with ECC memory for extra reliability and not intended for datacentre use. As an NVIDIA Elite Partner, we are qualified to discuss all datacentre enquiries and have Proof of Concept datacentre ready products available.

If you don't see the exact spec you want and would like us to custom build a Data Science Workstation for you please click HERE.

View:

How fast are Scan’s Data Science Workstations for deep learning?

We have benchmarked our pre-configured Data Science Workstations in two of the most popular frameworks, Tensorflow and PyTorch, so you can compare the performance of the workstations against each other.

The results from benchmarking Tensorflow, which are displayed as the rate of images per second, and PyTorch, which are displayed as the rate of Toks per second, clearly show the massive speed advantage the dual-GPU workstations (Q280X, Q264X, Q248X) have over the single-GPU workstations (Q136X, Q120X, Q116X).

A word of caution interpreting the results. You would expect the training rate for the workstations with the RTX 8000 GPUs to be faster than the workstations with the RTX 6000 GPUs. The reason this is not the case in the graphs is that the data we’re using isn’t large enough to take advantage of the extra VRAM the RTX 8000 has compared to the RTX 6000. And because the RTX 8000 runs at a slightly slower clock speed than the RTX 6000, the end result is a slightly slower training rate with a small data sets. However, if you are working with a larger data set the RTX 8000 would be much faster than the RTX 6000 as it has double the VRAM.

NVIDIA Data Science Workstations

View our range of Nvidia certified Data Science Workstations using enterprise grade Nvidia Quadro GPU accelerators. If you can't find the exact specification for your workload, then explore our configurator below to build a custom workstation.

Configure Systems

3XS Dev Box Pro Workstations

View our fixed spec range of 3XS Dev Box pros powered by GeForce RTX GPUs. Alternatively, if you can't see what you're looking for, view our configurator to custom-spec a workstation to meet your expect component requirements.

Configure Systems

NVIDIA Data Science Workstations

View our range of Nvidia certified Data Science Workstations using enterprise grade Nvidia Quadro GPU accelerators. If you can't find the exact specification for your workload, then explore our configurator below to build a custom workstation.

Configure Systems