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.
Why Do Data Scientists Need Better Workstations?
Data scientists spend most of their time working with large data sets in CPU-intensive Python libraries and algorithms—workloads that can make most data science workstations choke. Today’s workstations are overbuilt for model training and underbuilt for memory-intensive data manipulation.
We designed Intel-based data science workstations to make data-intensive AI and machine learning workflows fast, fluid, and responsive. With up to 6 TB of memory and workload-matched CPUs, these workstations can run truly massive data sets in memory and shave hours off the most time-consuming tasks in AI development.
Intel-Based Data Science Workstation Designs
Intel-based data science workstations come in three form factors—laptops, desktops, and towers—with a range of CPUs and memory capacities for specific data set sizes.
Data Science Laptops for 32–64 GB Data Sets
Remarkable performance for mobile AI development and data visualization.
- Intel® Core™ or Intel® Xeon® processor with Advanced Vector Extensions (AVX)
- Up to 128 GB of DDR4 DRAM
Data Science Desktops for 64–512 GB Data Sets
Excellent performance per dollar for preprocessing and analytics on midsize data sets.
- Intel® Xeon® W processor with AVX
- Up to 512 GB of DDR4 DRAM
Data Science Towers for Up to 6 TB Data Sets
Maximum performance for manipulating large data sets, machine learning, and decision support.
- Intel® Xeon® Scalable processor with AVX
- Up to 6 TB of combined DDR4 DRAM and Intel® Optane™ persistent memory (PMem)
Choose Your Intel-Based Data Science Workstation
Intel-based data science workstations ship in multiple configurations and a range of prices from Intel partners and manufacturers.
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 |
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 |
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 |
Run Faster with the Intel® oneAPI AI Analytics Toolkit
We’ve optimized the most-popular tools in the PyData ecosystem for Intel® architectures and bundled them all in the Intel® oneAPI AI Analytics Toolkit. These drop-in tools are ready to run, so you can work faster with little to no coding.
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.
Notices and Disclaimers
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. Learn more at intel.com/PerformanceIndex.
Pandas, scikit-learn, and TensorFlow acceleration achieved using the Intel® Distribution of Modin. For details, see intel.com/content/www/in/en/developer/articles/technical/code-changes-boost-pandas-scikit-learn-tensorflow.html#gs.mdyh9o.
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