Master Data Science Workflow
These specially designed workstations combine large memory span, numerous expansion slots to connect multiple devices, and handpicked CPUS designed to meet the unique demands of Python-based data scientists and data analysts like you.
Data Science Workstation: Superpowers Included
As a data scientist, you spend most of your time wrangling medium and large data sets in CPU-intensive Python libraries and algorithms - this brings most workstations to their knees.
That's because your workstation is probably overbuilt for model training yet underbuilt for memory-intensive data transformation.
Avoid memory errors when trying to load and explore data - a common experience when Pandas functions require more memory than your machine or cloud instance has available.
Faster NumPy/SciPy Compute Than Previous Generation
Linear algebra is the foundation of numerics and data science. The numerical computing tools provided by NumPy and SciPy allow the data scientist to work through numerical analyses and functions and a variety of machine learning models and mathematical formulas. iBench is a benchmark that tests stress commands in linear algebra of common algorithms used in NumPy and SciPy such as dot, det, inv, lu, qr, and svd. Performance is measured in seconds (lower is better). Compared to 3rd generation Xeon-W3275, 4th generation Xeon-W3495X performed 25% to 75% faster in the latest iBench tests.1
Supercharge Data Science
We designed Intel-based data science workstations to make data-intensive AI and machine learning workflows fast, fluid, and responsive. With up to 8TB of memory in dual-socket systems and workload-matched CPUs, these workstations can run medium to large data sets in memory and shave hours off the most time-consuming tasks in AI development.
Recommended Intel-Based Data Science Workstations
Intel-based data science workstations come in three platforms - mobile, mainstream, and expert - with a range of CPUs, memory capacities, and PCIe expansion slots.
Data Science Mobile Platforms for 32GB-64GB Data Sets
Remarkable performance for mobile AI development and data visualization.
- Intel Core HX-Series with up to 24 core (8P+16E).
- 128GB DDR5 DRAM for all platforms.
- Recommended SKUs:
- i9-13950HX (24 cores)
- i7-13850HX (20 cores)
Data Science Mainstream Platforms for 64GB-512GB Data Sets
Excellent performance per dollar for preprocessing and analytics on mid-size data sets.
- Intel Xeon W-2400 Processors with up to 24 cores unlocked.
- Up to 2TB of DDR5 RIDMM.
- Recommended SKUs:
- W7-2495X (24 cores)
- W5-2465X (16 cores)
Data Science Expert Platforms for up to 8TB DDR5 for Dual Socket Platforms
Maximum performance for manipulating large data sets, machine learning, and data analytics.
- Intel Xeon W-3400 Processors with up to 56 Cores.
- Up to 4TB DDR5 RIDMM for W-3400 series and up to 8TB DDR5 for dual socket Xeon SP 4th Gen platforms.
- Recommended SKUs for single socket platforms:
- W9-3475X(36 cores)
- W7-3455 (24 cores)
- W5-3425X (16 cores)
- Recommended SKUs for dual socket platforms:
- 6448Y (32 cores)
- 6442Y (24 cores)
- 6444Y (16 cores)
Run Faster with the Intel® oneAPI AI Analytics Toolkit
We've optimized the most popular tools in the Python ecosystem for Intel architectures and bundled them in the Intel oneAPI AI Analytics Toolkit to ease your experience in building your data science environment and to boost the performance of these tools. These drop-in optimizations are ready to run so you can work faster with few to no coding changes.
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.
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
As estimated by measurements made using Intel validation platform comparing Intel Xeon w9-3495X versus Intel® Xeon® W-3275 on NumPy/SciPy – Inv, N=25000
See intel.com/performanceindex for configuration details. Results may vary.
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/us/en/developer/articles/technical/code-changes-boost-pandas-scikit-learn-tensorflow.html#gs.mdyh9o.
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