Why Data Scientists Are Increasingly Using Z by HP Workstations For Their Workloads

“Pressure to convert massive volumes of data into real-time actionable insights has triggered a data race—and if you’re not in it, you’re already losing.”

Datasets are getting larger by the day and unpacking them for insightful business leverage has become tedious. Data science leaders across many organisations have started looking out for solutions that can take this burden off their shoulders. At the cusp of this rising challenge sits HP whose cutting edge workstations are empowering data scientists around the world to explore multi-billion record datasets in real time.

When it comes to workstations, HP has been leading the roost for over a couple of years now. The Z series workstations especially, by HP, pack a punch with an on board NVIDIA graphic unit among other state of the art components.


Sign up for your weekly dose of what's up in emerging technology.

But Why Choose A Workstation At All

There is no doubt that the proliferation of cloud services thanks to AWS, GCP and Azure have democratised data science. These cloud providers offer a wide range of services to derive intelligence for businesses. So, are all the organisations picking cloud over on-prem? The answer is NO. The reasons can be many. One of them is ‘security’.

For instance, Indian based self driving startup Swaayatt Robots doesn’t use cloud for the same reasons. “You might ask why not use the cloud. Regarding the hardware, we always go with GPUs. The data is immense. We cannot use the cloud because of the confidentiality of what we do. We have around 1.5 million images that we train on. It takes around 14 days to actually train that network. 

Download our Mobile App

For example, we have developed deep energy maps for contextual segmentation of the surroundings of the self-driving vehicles to help them better perceive their environments in all conditions. We train a neural network with a loss function, an advancement that I made for semantic segmentation. So now, the moment you put your network in a cloud, your loss function is up there too. And that’s very confidential for us. I mean, that’s our entire USP ,” said Swaayatt  founder, Sanjeev Sharma when asked about cloud.

According to HP, the arrival of 5G networks and a boom in connected devices as part of the Industrial Internet of Things (IIoT) will produce vast quantities of real-time data—all of which will need to be rapidly analyzed to inform timely business decisions. In a world of emerging technologies and powerful new analytics models, speed is as critical as accuracy—and in this world, the cloud is going to fall short.

Tap Into The Combined Powers Of HP & NVIDIA

HP designed its Z line of products so that data scientists can maximize productivity, cut down latency and lower the cost of data science projects with these workstations built to ensure the highest level of compatibility, support and reliability. 

To do this, HP’s Z family takes advantage of NVIDIA’s world class GPUs. NVIDIA has been pioneering the hardware technology for a while now. HP is leveraging Quadro’s unique capabilities to offer its customers a seamless service of managing ML workloads.

The Quadro series especially, the ones which are mounted on the HP Z workstations, are engineered to accelerate any professional workflow. NVIDIA offers the world’s most powerful GPU for visualization, large memory, advanced features, optimized drivers, over 100 software certifications, and IT management tools, Quadro delivers an unparalleled desktop experience. 

Data scientists can leverage the power of Quadro RTX to their workflow with 96 GB of ultra-fast local memory on desktop to handle the largest datasets and compute-intensive workloads from anywhere. 

“Edge includes mobile workstations and devices like the Jetson Nano…will be out on the edge collecting, filtering, and making data so that the cloud isn’t the endpoint for everything. This means you’re not hung up waiting for all the data to be processed up to the cloud in order to make a business decision,” says Jared Dame, Director of AI and Data Science, Z by HP.

NVIDIA-powered data science workstations come with a comprehensive stack of tested and optimized data science software built on NVIDIA CUDA-X AI. This stack features RAPIDS data processing and machine learning libraries, NVIDIA optimized XGBoost, TensorFlow, PyTorch, and other leading data science software, providing enterprises with accelerated workflows for faster data preparation, model training and data visualization.

Let’s take a look at two of the popular HP workstations powered by NVIDIA: 


  • HP’s best-selling workstation. Perfect for small ML workloads of <100 GB.
  • 64 GB Memory.
  • 2.5 TB Total Storage.
  • Graphics Card: NVIDIA Quadro P5000


  • World’s most powerful workstation built for large, complex workloads of >100 GB.
  • Memory: 192 GB
  • Total Storage: 5.25 TB
  • Graphics Card: 3x NVIDIA Quadro GV100.
Source: HP

The added advantage with Z by HP is that the data scientists get to access full stack pre-loaded data science software tools. They can get started immediately instead of wasting many days for installing all the required tools.

Support independent technology journalism

Get exclusive, premium content, ads-free experience & more

Rs. 299/month

Subscribe now for a 7-day free trial

More Great AIM Stories

Ram Sagar
I have a master's degree in Robotics and I write about machine learning advancements.

AIM Upcoming Events

Early Bird Passes expire on 3rd Feb

Conference, in-person (Bangalore)
Rising 2023 | Women in Tech Conference
16-17th Mar, 2023

Conference, in-person (Bangalore)
Data Engineering Summit (DES) 2023
27-28th Apr, 2023

3 Ways to Join our Community

Telegram group

Discover special offers, top stories, upcoming events, and more.

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Subscribe to our Daily newsletter

Get our daily awesome stories & videos in your inbox

All you need to know about Graph Embeddings

Embeddings can be the subgroups of a group, similarly, in graph theory embedding of a graph can be considered as a representation of a graph on a surface, where points of that surface are made up of vertices and arcs are made up of edges