If you know the size of your typical dataset and the type of data analysis you do, finding the best laptop for data science for you is pretty straightforward. For example,

A) If you, like most data scientists,  work with R & Pandas to run data sets that can fit in memory (4GB-16GB) to fit non-deep learning models. Any modern laptop that can be upgraded to 16GB RAM will do. Optionally, you can speed up the process by choosing the fastest CPU (the M2 & M1 CPU chips are the fastest as of 2023). 

B) If you work with parallel processing libraries that make use of GPU cores. Ex: deep learning. You want a laptop with 6GB vRAM for NLP (text data) and as much vRAM as possible if working with CV (image data). Not gonna lie a desktop with a 3090Ti would be a better choice for the latter.

C) Optionally, computer clusters (see featured image) allow you to train or process any data  (deep learning, neural networks, machine learning, etc) regardless of size and complexity hundreds of times faster than on any computer you can buy.  You can use ANY laptop of your choice to connect to these services.

Now, before we get to the best laptops for data science…

I will make a summary of the last section and talk about the ideal hardware specs for data science. 

Best Laptop Specs for Data Analysis

The motto for data analysis is “With greater data sets comes greater insights”.

Greater data sets will also require more hardware…usually RAM memory.

RAM’s the #1 most important hardware resource for data science.

RAM
As data sets grow larger, RAM becomes the first bottleneck. If you have 2x RAM of your biggest data set, things can speed up an order of magnitude because all your processing is in-memory(RAM). 

16GB RAM: bare minimum for data scientists. Not going to find it out of the box on budget (350-600 dollar) laptops but you can always upgrade RAM (some can be upgraded up to 32-48GB).

CPU
Faster CPUs are always good but since most CPUs are way too fast, RAM will become the main bottleneck long before CPU comes into play.

Here’s what I mean, if a CPU can process 10**5 pieces of data sets per second and if 32GB RAM can only serve 10**4 pieces of data per second, what’s the point of buying a faster CPU?

Assuming you have the luxury to choose a fast CPU AND you have maxed out on RAM memory:

If working with R & Python, choose the CPU with the highest clock speed (algorithms are mostly single-threaded).
Otherwise, grab a CPU with 8 cores as the limit (this is the maximum on laptops anyways).

The fastest CPU for most  CPU intensive algorithms and libraries is the M2 Max MacBook Pro. Any M1 & M2 chip beat Intel & AMDs on benchmark tests.

GPU

NVIDIA CUDA: If you WANT to work with deep neural networks or parallel.NN (parallell computing) algorithms that have process IMAGES then you want a dedicated GPU with as many shaders or cores you can afford & as much vRAM as possible. On laptops, this is the 3080Ti (16GB vRAM) but you’ll get your best bang of your buck with a desktop that has a 4090Ti. Note that the data set must fit into vRAM rather than RAM in this scenario.

If you can’t get a dedicated GPU (or can’t afford it) and you’re getting started, don’t sweat it. Most data scientists use cloud services for these kind of processing and you should too.

SSD
 Storage speed (SSD Type) has little impact, if any, on the data crunching process but if you want to maximize speed when transfering files from drive to drive: the fastest is PCIe NVMe 4.0. 

Keyboard
Good keyboards on laptops are not easy to find and usually expensive. If you don’t feel comfortable with the built-in keyboards no worries, just get an external keyboard. External mouse or ball trackpad is a MUST, you dont want RSI and tendonitis.

Display
Min FHD 15” screen: Chances are you’ll be either ssh’ing into a more powerful machine or using the cloud later at some point so the extra screen space becomes super useful to see longer pieces of commands at a time.

OSX
Mac vs. Windows vs. Linux – the best OS for data science is either a Linux-based machine or an OSX apple computer.

Thus if you can afford, pick a laptop that can support a Linux Flavored OS seemlessly like a Lenovo ThinkPad if going for windows laptops since you may use linux entirely (though most will be fine with a Linux virtual machine on a Windows-based laptop). 

Top 6 Best Laptops for Data Science

In this list I’ve tried to include a laptop for EVERYONE: beginners, students and Data Scientists (those into parallel programming, machine learning, deep learning and those Using AWS/Cloud Services,etc.)

Just read the descriptions carefully and you’ll be sure to find your best pick.

1. HP Victus Gaming 3

Best Budget Laptop for Data Science

  Core i5 12500H

  8GB DDR4

  3050Ti RTX (4GB vRAM)

  512GB PCIe NVMe SSD

  15.6” 60Hz Full HD IPS

  5.04lbs

  5 hours

This is a sort of all-round basic laptop for data science. 

What I mean by this is that it has ALL the hardware you need for data science (a dedicated GPU and the minimum RAM: 8GB) and it’s not overly expensive as the next laptops we’ll go over.

So this is sort of useful for anyone getting started with data science or anyone who wants to experiment and perhaps even run some ML or parallel process dependent algorithms that aren’t overly heavy (for testing purposes).

Why choose the HP Victus over any other 3050Ti laptop?

Now although there’s a whole myriad of 3050Ti laptops on the market around the same price. There are two reasons WHY this is the BEST 3050Ti laptop for data science:

• It’s got the latest 12th gen Core i5 CPU. Though not a MUST nor a significant performance boost to data science algorithms, it is still a nice addition to the arsenal.
• RAM upgradeability. It supports up to 64 GB RAM!

RAM: 8GB RAM (Up to 64GB)

Leaving upgrades aside, 8GB RAM is enough for simple statistical and ML/DL models of small data sets. 

Also enough to run any Data Analysis package and software (R/MatLab/SAS,etc).

Now what do I mean by a small data set?

Anything around 300k rows with 4 variables each or a A data set that weighs around 300MB. 

Now…Windows 11 takes around 4GB. Background programs + your software: 1GB. That leaves you with 3GB RAM left, x10 times the size of a small data set.

Maximum Data Set for 8GB RAM

The maximum size would be 3GB.

Which is approx 3G/300MB~10*300k rows=3000K rows with 4 variables.

If you’re getting started with data science, it is very unlikely you’ll work with a dataset this big. If you do encounter datasets much bigger (10GB), then you can use the cloud regardless of what laptop you use.

Q: But but but…I don’t want to use the cloud. I want to use a laptop to run data sets much bigger!!

The solution is simple and this is why I picked the HP Victus over all other laptops.

Since this laptop has a dual-RAM slot and has no RAM soldered to the motherboard that means you can replace the 8GB RAM with two 16GB RAM sticks giving you a total of 64GB RAM.

Which basically lets you work with data sets close to 60GB ! This is equivalent to 3000K*20=60 000K rows = 60 MILLION ROWS with 4 variables each.

If you do happen to run into much bigger data sets (which isn’t likely) , then you can use the cloud!

You’ll have no other choice really because not even desktops support RAM into the 300GB gigabytes. Data sets this big for CPU-based data crunching is extremely rare. 

GPU: 3050Ti  Parallel Processing

It is no secret that parallel computing tasks like deep learning and machine learning will be faster with more cores and that today (2023) most libraries make use of GPU cores instead of CPU cores. If you’re into any field that uses GPU Cores and have a budget under 750 dollars, I want you to take a GOOD look to the table below.

It should be pretty obvious WHY I listed the slightly more expensive 3050Ti over any of the other ‘somewhat cheaper’ laptops with dedicated GPUs: you get x2 the amount of CUDA cores which means you’re computing performance will be increased two fold. Now that’s only speaking for small data sets for machine/deep learning (4GB vRAM) which will usually be text data or testing smaller batches of image data.

Deep learning/machine learning with IMAGE data sets  that go beyond 30GB will always require you to use Cloud Computing services or a desktop as no laptop has that much vRAM.

2. M2 MacBook Pro

Fastest Laptop For Data Analysis

  M2 Pro Chip 10 core (Up to 12)

  24GB Unified Memory (Up to 96)

  10 core GPU (Up to 19)

  512GB-2TB PCIe SSD

  13” Retina (Up to 16”) 

  3lbs (5lbs)

  13 hours

Now this is a very controversial laptop but imo it is the best laptop for data science as of 2023.

I’m sure you’ve heard about the M1 & M2 chips being ‘optimized’ for machine learning, deep learning and neural networks and that they are faster than the Intel Chips for these same tasks and this is very true:

• For deep learning, machine learning, neural networks it significantly outperforms “mobile” CPU from Intel/AMD even Core i9 laptops and this is due to the ‘faster and easier’ access to memory (RAM). It is said to be ‘unified’ that means that the GPU has access to all the RAM memory unlike normal laptops which only have access to the memory available in vRAM. This means if you upgrade the MacBook Pro or Air to 16GB or 64GB, you’ll have access to all that memory for GPU-based algorithms like deep learning.
• M1 & M2 chips have more cores than any other Intel CPU and this is ONE of the reasons reason why they’re much faster for CPU-based algorithms. The second reason being that the chips’s architecture have been optimized for “machine learning”.

There’s TWO big catches though:

• For GPU processing purposes, not many libraries are compatible with the M1 & M2 chips. Here’s the applications where it is both compatible and where the Apple Chips outperform Intel & AMD CPUs.
• If using deep learning with a NVIDIA GPU, as long as the memory size is the same. NVIDIA GPUs will outperform ANY apple M1 & M2 chip as the number of cores on modern GPUs are much higher. The only reason some benchmarks like TensorFlow PyTorch seem to run better on the M1 & M2 chips compared to Windows laptops with NVIDIA GPUs is because the lack of memory of laptop GPUs. If you put a desktop 3080Ti with 24GB vRAM vs the M1 & M2 chips with 24GB vRAM, then the story will be entirely different. 

So why pick the MacBook M1 & M2 models?

Well, I wouldn’t say you have to pick these models specifically. For most data science purposes (deep learning and GPU parallel tasks out of the question) you could pick older models too. Why? Because they all have:

• The UNIX-like environment. OSX is the most work-efficient OS for data science, just as good as a Linux system if not better.
  It’s not just about how python just works best on a UNIX system but also how all the software packages and languages are readily available out of the box but the ease of programming through the terminal too.
• Unrivalled battery life & portability:  if you have to ssh into a server. You can be outside of your hose and still check the progress and upload more chunks of data as soon as you get close to a WiFi spot. 
• M1 & M2 MacBook Air & 2015-2023 MacBook Pros have a retina resolution which increases the amount of screen space, super useful to visualize data, get a bigger picture of your data & multitask.

If I can afford either the M1 & M2 MacBook Air or MacBook Pro, which one do you recommend?

It really comes down to your hobbies or out of data science activities  If you’re only using this laptop for Data Science purposes grab the MacBook Air. If you’re also going to use this for say for video or photo editing, gaming, etc, then you grab the MacBook Pro.

What about data science applications? What’s the difference between the two?

Truth be told, both are better than any windows laptops for CPU-intensive algorithms and computations. The M2 Max MacBook Pro will be the fastest one though. Either way neither of those models have a dedicated  NVIDIA GPU which have thousand of cores for parallel processing so if you are working with deep learning then you dont want a MacBook Air unless you’re fine using the Cloud Service.

3. Lenovo ThinkPad T15p 

Best Windows Laptop for Data Analysis

  Core i7 12800H

  16GB DDR5 (Up to 64GB)

  RTX 3050Ti 4GB vRAM

  512GB

  15” FHD IPS 

  5lbs lbs

  6 hours

ThinkPads can support RAM anywhere from 8GB to 64GB RAM. The upper limit for most however is 48GB.

If you want a 64GB RAM thinkpad, 64GB RAM being the highest laptops can support, you’ll have to head over to the official Lenovo website or buy the laptop I’m featuring here (this is the only ThinkPad, besides workstation thinkpads, that support 64GB RAM to my knowledge).

DDR5 : fastest RAM

We’ve already talked about what kind of data set 8GB RAM and consequently 30GB can support. So let me just add another tip here and it has to do with  RAM ‘generation’. You’ll find 3 generations as of 2023 when shopping for laptops: DDR3, DDR4 and DDR5.

The HP Victus out of the box supports 8GB DDR4 but once you do the upgrade you can install 64GB DDR4, there isn’t a significant speed difference when it comes to feed data to the CPU between those two generations however once you work with very large data sets, the difference is SIGNIFICANT.

Now, the HP Victus does not support DDR5 which  is the latest generation and the difference is SIGNIFICANT for non-data science purposes and once you talk about data science and large data sets there is even a more SIGNIFCANT performance again.

DDR5 cannot be found on just about any laptop with a 12th Intel Core or 6th gen AMD CPU, the motherboard used by manufacturers HAS to have a pin socket that supports DDR5. In other words, there are very few laptops that support DDR5 with this Lenovo ThinkPad T15 P1 Gen 3 being one of them as you can read in the specification sheet here.

I’m including CPU from four generations here you’ll find them on modern latpops even those brand new in 2023. 
Do note that the “base” & “turbo” columns quantify the maximum clock speed.

Notice how the clock speeds are very close to each other despite some CPUs being way more expensive and more recent. 

For the average basic work done on a laptop for Data Science all of these clock speeds are fast enough. 

If you want to maximize CPU power for parallel scripts & algorithms in data science, you only have to pick an 8 core CPU as some algorithms and tasks will take advantage of CPU cores (Up to 8 on average) if they do not support GPU core usage.

GPUs are mostly useful for Machine Learning, Deep Learning, neural networks & image processing.

That doesn’t mean you will not find them useful outside of those fields.

As of 2023, parallel processing is finding its way in more and more data science applications each passing year so a dedicated graphics may be somewhat of a good investment for basic data analysis thus what’s said next MAY apply to those applications too.

NVIDIA vs AMD: CUDA Cores

All of this GPU hype is limited to graphics card from NVIDIA because that’s what developers work with when writing algorithms AND NVIDIA also designs their graphics card to find its way in the data science industry. 

Although you can use AMD GPUs and perhaps any other GPUs for machine learning , deep learning ,etc, it may not be a 100% solution and you may have compatibility issues with MANY packages and scripts.

You can read all about how NVIDIA GPUs are useful and why AMD GPUs may not be as useful in the following article: “The best graphics cards for Machine learning“.  But again it basically comes down to “CUDA” core technology being considered by developers at the time of writing and developing scripts and algorithms. Likewise, NVIDIA also designs their GPU’s architecture with these applications in mind.

Libraries & Packages with CUDA Compatibility

Pretty much all of the deep learning libraries and most machine learning libraries (tensorflow & torch) use CUDA cores from NVIDIA GPUs.

In fact, deep learning algorithms ONLY use the GPU instead of the CPU. Due to this, algorithms that took a week in the past now take less than a day with a CPU.  Image Processing has been using GPUs since it’s infancy but now it is way more efficient at it.

Some algorithms and packages in Machine learning may use the CPU as we discussed before.

Q: So exactly which Data Science Software/Service/Tools make use of NVIDIAs CUDA core technology?

Within deep learning & neural networks ALL algorithms and libraries makes use of CUDA core technology EXCEPT legacy software.  As for machine learning about 90% of libraries and packages are GPU-based.

Concerning applications outside of these fields you should double check whether or not your library or set of tools make use GPU or support parallel processing. Many people think using a GPU or using a cloud service that has a stack of GPUs (Ex: AWS) will massively speed up computation with parallel processing only to find out it doesn’t.

Q:  We are talking about desktop GPUs right? Laptop GPUs are useless.

You’ve probably came across some articles claiming laptop GPUs are useless because they are much much weaker than desktop GPUs and that’s partly true but in no way that implies that laptop GPUs are useless for data science.

If you came across an article claiming laptop GPUs being useless , it was probably written 15 years ago. 

Today’s GPU (in fact even since the 10th generation line of GeForce GPUs , 2017 or so) are pretty much the same kind of GPUs you find on desktops EXCEPT that they have their TDP reduced because laptops cannot accomodate a decent cooling system to allow the GPU to hit its highest clock speeds. But this reduces their performance up to 30-50%. Thus if you want the best performance for GPU parallel processing tasks you want a desktop GPU over a laptop GPU.

How to Pick a GPU: vRAM & CUDA Cores

vRAM & DataSet Size

As long as you have approximately the same amount of vRAM as the typical size of your dataset then processing speeds will be fast. 

For example, if you have a 8GB dataset, you want a GPU with 8GB vRAM memory as a minimum. This is a small size compared to real world useful data which are in the 50-100GB and for which either desktops become ideal or computer clusters.

CUDA Cores & Data Crunching Speed

Once you fit all your data into vRAM, the GPU with more CUDA Cores will be the fastest one. For example, in the table below there are several GPUs with 4GB RAM, out of these the 3050Ti will be the fastest for deep learning due to have almost 2x the amount of “procesing units”.

Which GPU to pick? 

It depends on what your focus is RIGHT NOW:

A) In the scenario where you’re getting started with deep learning/machine learning through guides, videos and tutorials. Usually tensorflow algorithms, then there’s no need to compile image net or visual models on your GPU. This means a 4GB vRAM GPU like the 3050TI or any of the 4GB vRAM GPUs should be fine.

B) If you want to work on your own project, you will run significantly larger models. If the your project is simple, then you should be okay with a laptop GPU. If you can afford it I would recommend a 8GB-16GB vRAM GPU (for laptops).

C) For large scale projects for research and companies where products are being developed for a company. You have to use a computer clusters which have GPUs like the NVIDIA A100. 

M1 & M2  vs NVIDIA GPUs: Deep Learning & Machine Learning

In the video above it may seem that the MacBooks here beat the RTX chips for Machine & Deep Learning however this is only due to the lack of vRAM of RTX GPUs found on laptops. The MacBooks have “unified memory” that is both the CPU & GPUs SHARE the same RAM (there is no vRAM on these MacBooks) thus easily outperforming RTX laptops as shown in the video.

Now the test can be carried with smaller data sets as shown below:

In the scenario where the datasets are small enough for RTX GPUs , the M2 Chips will outperform every NVIDIA GPU. 

64GB Unified Memory vs 24GB vRAM Benchmarks

The problem with the above benchmarks is that the models are too small and in the instance where they are large, the MacBooks have more ‘vRAM’ so to speak thus the comparison is not fair.

Currently, the M2 MacBook supports up to 64GB unified memory, that means you can train datasets that weight that much through the GPU for machine or deep learning. I have not carried the benchmarks but I would expect the M2 Max Pro Chip GPU  with 64GB Unified to beat the 3080Ti Laptop GPU with 16GB vRAM. 

However, do note that the M2 MacBook with the M2 Max Pro chip is extremely expensive and you can get a 24GB vRAM GPU (4090Ti) at a much cheaper price (this one would definitely outperform the macbook M2 Max Pro chip) for datasets around that size.

3. Storage

Size: 256GB min

Obviously, if you handle datasets in the gigabytes range and store them on your hard disk drive, you want at leasta 256GB or even a 512GB.

If we are talking about reducing the time it takes to process datasets, how much storage you get doesn’t matter. 

Type : Solid State Drive

For data processing, choosing a solid state drive (or the fastest solid state drive PCIe 4.0) will somewhat speed up the data crunching process because solid state drives can ‘feed’ data faster to RAM and CPU.

If we are talking about transfering data from storage to RAM, the difference is MINIMAL between all types of Solid State Drives.

In the scenario where your data set is much bigger than the RAM you have available and the CPU has to resort to do the processing straight out of the storage drive, then yes choosing the fastest Solid State Drive (PCIe 4.0 as of 2023) will make a significant difference.

However, the performance of CPU-RAM data processing is WAY faster than CPU-SSD data processing so you WANT TO AVOID running out of RAM.

Conclusion: the fastest solid state drive PCIe NVMe 4.0 isn’t something that’s a MUST for data science. As long as you. get ANY solid state drive you should rip all the usual benefits of SSDs (boot up your machine in 5 seconds, instantaneous code look up, launch software in seconds, etc).

A) Using the Cloud

If you’re going to use the Cloud because your datasets are extremely large (100GB range) then you want to pick an SSD with as much storage as you can afford.

A 512GB SSD is a good start. As for uploading data to the cloud, it won’t be any faster with the fastest SSD. Ex: choosing a PCIe 4.0 won’t make uploading 100GB to the cloud any faster than a SATA III SSD.

Author Profile

Miguel Salas

I am physicist and electrical engineer. My knowledge in computer software and hardware stems for my years spent doing research in optics and photonics devices and running simulations through various programming languages. My goal was to work for the quantum computing research team at IBM but Im now working with Astrophysical Simulations through Python. Most of the science related posts are written by me, the rest have different authors but I edited the final versions to fit the site's format.

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