Data Science Workstations for the Real World

Designed by data scientists, these new workstations combine large memory capacity and handpicked CPUs that meet the unique demands of Python-based data science tools and workflows.

Intel-Recommended Configurations

Type

Model

Processor

Memory

Disk

Laptop

7760

Intel® Xeon® W-11855M

128 GB

2 TB SSD

Desktop

5820

Intel® Xeon® W-2295

512 GB

2 TB SSD NVMe
(boot drive)

Desktop

7920

2 x Intel® Xeon® Platinum 8260L

4.5 TB

512 GB DRAM

(8 x 64 GB)

 

4TB Optane™ PMem

(8 x 512 GB)

2 TB SSD SATA
(boot drive)

Intel-Recommended Configurations

Type

Model

Processor

Memory

Disk

Laptop

G8

Intel® Xeon® W-11855M

128 GB

2 TB NVMe

Desktop

Z4

Intel® Xeon® W-2295

512 GB (requires higher-watt chassis)

960 GB SSD SATA (boot drive)

Desktop

Z8

Dual (2S) Intel® Xeon® Gold 6242R

~1.7 TB

384 GB DRAM

(12 x 32 GB)

1.5 TB Intel® Optane™ PMem

(12 x 128 GB)

NVMe

Desktop

Z8

2 x Intel® Xeon® Platinum 8260L

~6.6 TB

768 GB DRAM

(12 x 64 GB)

 

6 TB Intel® Optane™ PMem

(12 x 512 GB)

NVMe

Intel-Recommended Configurations

Type

Model

Processor

Memory

Disk

Laptop

P1 Gen 4

Intel® Xeon® W-11855M

64 GB DRAM

2 TB SSD

Laptop

P15

Intel® Xeon® W-11855M

128 GB DRAM

2 TB SSD

Laptop

P17

Intel® Xeon® W-11855M

128 GB DRAM

2 TB SSD

Desktop

P520

Intel® Xeon® W-2295

512 GB DRAM (requires higher-watt chassis)

NVMe

Tower

P920

Intel® Xeon® 6240L

4.5 TB

512 GB DRAM

4 TB Intel® Optane™ PMem

NVMe

Frequently Asked Questions

There are two primary factors to consider when choosing a data science workstation: which tools and techniques you use the most and the size of your data sets.

When it comes to data science frameworks, higher core counts don’t always translate into better performance. NumPy, SciPy, and scikit-learn don’t scale well past 18 cores. On the other hand, HEAVY.AI (formerly OmniSci) will take all the cores it can get.

All of the Intel-based data science workstations use Intel® Core™, Intel® Xeon® W, and Intel® Xeon® Scalable processors that excel at data science workloads in real-world tests. You’ll get best-in-processor-family performance from all of them, which makes memory capacity your most important choice.

Data science frameworks balloon memory footprints two to three times. To get your baseline memory needs, examine your typical data sets and multiple by three. If you can work with 512 GB or less, you can get excellent performance in a desktop machine. If your data sets tend to be above 500 GB, you’ll want a tower with 1.5 TB of memory or more.

GPU accelerators shine at deep learning model training and large-scale deep learning inference. However, for the bulk of data science work—data prep, analysis, and classic machine learning—those GPUs sit idle because most Python libraries for data science run natively on the CPU. You do need a graphics adapter to drive your displays, but not a GPU appliance.

Intel® Optane™ PMem provides large data capacities in a form factor that drops into a normal DRAM slot. PMem comes in 128 GB, 256 GB, and 512 GB modules vs. 32 GB, 64 GB, and 128 GB DRAM modules. With Intel® Optane™ PMem, you can pack far more memory into the same motherboard. Learn more about Intel® Optane™ PMem.

The cloud won’t give you the best performance unless you’re running on a dedicated VM or a bare metal server. Cloud instances present themselves as a single node, but on the back end, things are highly distributed. Your workload and data get split across multiple servers in multiple locations. This creates processing and memory latencies that degrade runtime. Plus, working with large data sets and graphs through a remote desktop is not an ideal experience.

Keeping the workload and data local, on a single machine, can deliver much better performance and a more fluid and responsive work experience.

You can, but you’ll burn immense amounts of time watching data shuffle between storage, memory, and the CPU. If you’re working in a professional environment, upgrading to an Intel® data science laptop or midrange desktop can be a time-saver. We intentionally tested and specced Intel® Core™-based data science laptops so that students, beginners, and AI makers could have an affordable option for developing and experimenting with open source AI tools.

You can run Python-based data science tooling faster on a standard PC using Intel-optimized libraries and distributions. They’re all part of the free Intel AI Kit.