Table of Content
- Best laptops for machine learning you can buy today
- Specs requirements for machine learning laptop
If only someone developed an algorithm that could give you a list of the best laptop for machine learning on a whim, right?
Luckily, this is where we come in.
We’ve put in the hard work and done the necessary research so that all you have to do is to choose the best laptop for machine learning that fits your needs, preferences, and budget the most.
Related Post: Best Laptop For Watching Movies
Best laptops for machine learning you can buy today
|Laptop Name||Editor's Score||Processor||Price|
|1. MSI Leopard GL65||10/10||Core i7|
|2. Lenovo Legion 5||9/10||Ryzen 7|
|3. Acer ConceptD 5 Pro||9.9/10||Core i7|
|4. HP Stream 11″||8/10||Intel Celeron|
|5. ASUS VivoBook 15||9/10||Core i3|
|6. Dell XPS 15 7590||9.5/10||Core i7|
|7. Lenovo ThinkPad P15||8.5/10||Core i7|
|8. Microsoft Surface 3||8.5/10||Core i7|
|9. Apple MacBook Pro (2020)||9.5/10||Core i9|
|10. OMEN by HP 15″||8/10||Core i7|
1. MSI Leopard GL65
- 15.6" FHD IPS-Level 144Hz 72%NTSC Thin...
- Intel Core i7-10750H 2.6-5.0GHz Intel...
- 512GB NVMe SSD 16GB (8G*2) DDR4 2666MHz...
- USB 3.1 Gen2 Type C 1 USB 3.2 Gen1 3...
- Win10 Multi-language Giant Speakers 3W x...
The MSI GL65 Leopard 10SFK-062 is a jack-of-all-trades kind of laptop.
The laptop doesn’t particularly excel at anything, but it checks all of the proverbial boxes, including coming at a reasonable price point. As a bonus, it has per-key RGB lighting.
This is a useful feature for machine learning professionals as it lets you color-code shortcuts for better productivity.
The MSI GL65 Leopard 10SFK-062 features the Intel Core i7-10750H processor.
This gives you access to as many as six cores and twelve threads, which can help you do all of the necessary computations simultaneously.
The laptop is also fitted with a 512GB NVMe SSD, which is just right around the amount of space that you’ll need for your data. As a bonus, the NVMe SSD has much faster seek and write times compared to traditional SSDs.
Lastly, running data sets and machine learning algorithms will be a piece of cake as you’re given 16GB of DDR4 RAM to play around with.
The 15.6-inch laptop is right in line with its competition in terms of dimensions (14.08 x 9.76 x 1.08 inches) and weight (5.07 pounds).
Admittedly, the 6-cell 51Whr battery is disappointing. You’d think that, with its weight, it’d feature a better and longer-lasting battery.
Because of this, you shouldn’t expect this laptop to last you for longer than an hour or two while working before needing to be recharged.
The 15.6-inch full HD display comes with an IPS panel with 72% NTSC and close to 100% SRGB.
The display quality is fairly good and bright. It has relatively thin bezels as well, which gives it a nice and sleek look.
Perhaps the biggest advantage of buying this laptop is that you get an Nvidia GeForce RTX 2070.
An Nvidia card with RTX technology is the ideal companion for machine learning learning applications. This is because most deep learning frameworks work natively with CUDA and are heavily supported by Nvidia.
The 8GB Vram of the RTX 2070 is also enough for most machine learning applications outside of enterprise-level ones.
Intel Wi-Fi 6 and Bluetooth 5.0 are both non-essentials for machine learning. However, these added features only help make the GL65 an even better buy.
The GL65 also offers some expandability. You can add in a standard 2.5” HDD or SSD for more storage and it can also support up to 64GB of DDR4 memory.
Outside of the hardware, perhaps the biggest draw of the GL65 is the Steelseries per-key RGB keyboard with anti-ghosting.
Outside of the mediocre thermals, it’s hard to find any kind of fault on the GL65 without nitpicking.
Sure, battery life is a bit low. Then again, the same can be said for pretty much every other gaming laptop. Not to mention, at its price point, you can’t realistically expect a longer battery life.
The fact is, what you’re paying for here is the RTX 2070.
Everything else is just a bonus.
2. Lenovo Legion 5
“Best laptop for machine learniing”
- Welcome to the next generation of gaming...
- Enjoy fast refresh and deep colors with...
- The NVIDIA GeForce GTX 1660Ti GPU is a...
- Get maximum performance via Dual Burn...
- The Legion TrueStrike keyboard with...
Multi-threaded applications like the ones used in machine learning and data science are where laptops like the Lenovo Legion 5 Gaming Laptop shine the most.
It looks much more plain and “normal” compared to our top pick. However, if performance is all that matters to you, the Legion 5 makes for a compelling choice. At the same time, it’s also noticeably cheaper.
Intel’s done well to close the gap in processing performance between their processors and that of their AMD counterparts.
Even so, there’s some noticeable performance difference between like, let’s say, the Intel Core i7-10750H and the Ryzen 7 4800H mobile processor of the Legion 5.
The Ryzen 7 4800H, for example, has 8 cores and 16 threads. It’s also built using a 7nm process. This makes it more energy-efficient with lower heat generated and it is much better-suited for the kind of multi-threaded performance expected in machine learning.
The Legion 5 is also fitted with 512GB NVMe SSD for faster seek and write times compared to HDDs and traditional SATA SSDs.
One thing that we’d like to note is that Lenovo specifies that the Legion 5 can only hold up to a maximum of 16GB of DDR4 RAM, which it already ships out with.
However, if you look up online, there are users reporting that they’ve had luck upgrading the memory up to 64GB. Although we haven’t been able to verify this ourselves, so take this information with a grain of salt.
With dimensions of 14.3″ x 10.2″ x 1″ and 5.5-pound weight, the Legion 5 is pretty pedestrian in this regard.
Legion 5 makes up for this standard design and weight with its battery life.
The 15.6-inch laptop is fitted with an 80Wh battery, which, Lenovo claims, can hold up to 8 hours of casual use on a single charge.
While we doubt that it will last as long when doing heavy machine learning tasks, we do believe that the laptop should last at least a couple of hours under heavy load without needing a recharge.
In addition to the rather large 80Wh battery, the Zen 2 architecture of the 4800H is widely considered to be energy-efficient.
The 15.6-inch FHD IPS display comes with a 144Hz refresh rate.
This feature isn’t nearly as important to machine learning, as, let’s say, the 100% sRGB for better visuals, but it’s nice to have. It can get a little too boring at times working all the time, so having the option to game and game well, at that, is always welcome.
At 300 nits, the screen isn’t the brightest, but because it’s matte, there’s very little glare.
You’ll notice this the most when you’re working, especially outdoors, as you’ll find it easier to see what’s on your screen compared to other laptops.
As for the graphics card, the Nvidia GTX 1660Ti is where the Legion 5 falls short compared to our top pick.
While it’s still a fairly powerful graphics card with 6GB of GDDR6 VRAM and a 192-bit memory bus that should be able to handle most machine learning tasks well, it doesn’t benefit from the “Tensor Cores”.
These cores are made specifically for machine learning and are only available on Nvidia’s RTX line of graphics cards.
Contrary to how it looks, the Legion 5 has backlighting options. It has a default white backlighting for the keyboard, with an optional 4-zone RGB backlighting for added customizability.
Unfortunately, 4-zone backlighting isn’t as useful for coding and programming as per-key RGB.
Personally, we prefer to turn off the lighting anyway.
The backlight is not particularly bright so, unless you’re doing machine learning tasks in dark rooms, you’ll want to turn it off. It’ll help you conserve battery as well.
As for typing on the keyboard, it feels the same as with any other Lenovo laptop outside of the ThinkPads.
This isn’t necessarily a bad thing. Lenovo makes good laptops with comfortable keyboards.
The Microsoft Precision trackpad also makes it possible to use the laptop productively without the need for a mouse. It’s also clickable, which some people prefer because of that tactile feeling.
It’s worth considering that the Legion 5 does come with an RTX 2060 model. That model is a better buy with a noticeable step-up in performance in gaming and with inference steps with a pre-trained model, among many other applications.
The only reason we don’t recommend that model is its price.
The difference in performance between the RTX 2060 and RTX 2070 is far bigger in certain applications, and the latter is found in our top pick at the price point as the Legion 5 with the RTX 2060.
With that said, we’re sticking to the GTX 1660Ti model, which remains a great value.
The GTX 1660Ti is a power-efficient graphics card with above-average FP32/16 compute capability, and again, as already mentioned earlier, with 6GB worth of GDDR6 VRAM, it’s great for deep learning.
3. Acer ConceptD 5 Pro
“Best Portable Workstation”
- 9th Generation Intel Core i7-9750H...
- NVIDIA Quadro RTX 3000 Graphics with 6...
- 15.6" 4K Ultra HD (3840 x 2160)...
- 32GB DDR4 2666MHz Dual-Channel Memory ,...
- Amber Colored Backlit Keyboard ,...
Acer ConceptD 5 Pro made it to the top of the list of the best laptoop for machine learning.
The ConceptD 5 Pro CN515-71P-75XP Creator Laptop is designed for content creators and graphic artists.
It’s equipped with NVIDIA Quadro RTX 3000 graphics, which is also an excellent choice for machine learning.
Because of this, and a myriad of other factors, we see the ConceptD 5 Pro as a hefty but worthy investment for machine learning engineers and other professionals in the same industry and/or field.
The ConceptD 5 Pro is fitted with the Intel Core i7-9750H, which is a 9th generation 6-core and 12-thread processor.
It packs plenty of processing power and can handle time-series data and other types of signals, as well as picture-processing tasks just fine.
You also get 32GB of DRR4 memory that you can upgrade up to 64GB and a 1TB NVMe SSD.
Being that this is a mobile design station, the ability to work outdoors and everywhere wasn’t the priority.
Still, even if that’s true, the ConceptD 5 Pro isn’t particularly heavy nor large at 14.31 x 10.01 x 0.91 inches with a 5.5-pound weight. But, you will want to work only in places where you can plug in a charger.
The 4-Cell Lithium-Ion battery is reportedly good for 6 hours of use, but we highly doubt that.
In terms of display, this is where the ConceptD 5 Pro shines.
Because this was designed for graphic designers in mind, you get to enjoy a 15.6-inch 4K Ultra HD IPS display.
The display also has a 400-nit brightness and is PANTONE Validated with testing and calibration done to make sure that it achieves a low Delta E average of <2 for life-like color accuracy.
TLDR; the display is beautiful and amazing, even if you’re not into graphic design.
Now, for the most important part.
The ConceptD 5 Pro has an NVIDIA Quadro RTX 3000 graphics card.
Earlier, we mentioned that RTX cards were ideal for deep learning applications because of their Tensor Core performance. However, Quadro cards are even better for this. This is because it doesn’t have performance degradation.
What happens is that Nvidia “purposely” lowers the performance of RTX cards so that they aren’t as good as Quadro cards in certain applications.
Being that this is a Quadro RTX card and an RTX 3000 series at that, it’s great for sparse neural network training and inferencing with huge performance gains over RTX 2000 series graphics cards.
The ConceptD 5 Pro is a nice looking laptop. It’s arguably the best-looking option on our list.
Everything about how the ConceptD Pro looks seems like it was designed so that professionals would love using it anywhere, even during meetings.
Even in darker environments, the keyboard has amber backlighting that isn’t too bright but isn’t too dark either. Also, typing on it makes for a comfortable and wonderful experience, which should make you forget how long you’ve been slaving on your computer.
For better productivity, the ConceptD Pro also gives you access to a wide selection of ports.
This includes a USB Type-C Gen 1 port with support for up to 5Gbps data transfer and offline charging.
If your primary concern is to get the best mobile graphics card for deep learning with enough processing power to boot, the ConceptD Pro is a very good choice.
This mobile workstation hits all of the right points and is fairly priced, all things considered.
4. HP Stream 11″
- 11.6 " diagonal HD (1366 x 768) SVA...
- Equipped With The Powerful and Latest...
- RAM is upgraded to 4GB memory for...
- Hard Drive is upgraded to 32GB eMMC...
- Windows 10 in S mode. 1-year Office 365...
How HP managed to cram a long-lasting battery with good audio and solid processing performance into the HP Stream 11 is nothing short of a miracle.
Of course, this isn’t the ideal laptop for machine learning professionals. It’s far too weak for that. However. If you’re still getting a feel for things, the HP Stream 11 is a great starter choice that will not disappoint.
You shouldn’t expect a lot of performance out of the Intel Celeron N4000, which only has 2 cores and 2 threads with no hyper-threading. Nor can you expect the 4GB DDR4 RAM and the 32GB eMMC to do actual machine learning.
Still, if you just want to learn basic machine learning, you can see it working on this laptop just fine.
We emphasized basic machine learning because that’s all pretty much the HP Stream 11 is good for.
That’s not meant to be an insult, but rather, a testament to how relatively capable it is.
For students who need something that they can use on the go, the HP Stream 11 is perfect.
This compact laptop measures 11.30 x 7.80 x 0.70 inches and weighs just 2.40 pounds with a battery that’s expected to last for over 8 hours of use, which is enough for an entire day at school or work.
This is one of the areas that the HP Stream 11 really starts showing its price.
The 11.6-inch HD display is rather dim and I wouldn’t recommend using it somewhere with glare, let alone outdoors.
The graphics card doesn’t do it any favors either. It uses Intel UHD Graphics 600, which won’t make any kind of machine learning training quick at all, and that’s only if you can get it to work in the first place.
The 32GB storage space is small, but if it’s any consolation, the HP Stream 11 is usually bundled with a memory card and adapter for added storage, which you can use via the card reader.
You also get 1-year of Microsoft Office 365 Personal, as well as fairly speedy cable options with a 1 x USB 3.1 Gen 1 Type-C port and 2 x USB 3.1 Gen 1 port.
You don’t always need to splurge to learn machine learning.
Numerous companies, like Amazon, Microsoft, and Google, have invested heavily into making cloud computing accessible for everyone, and having a laptop like HP Stream 11 is a good way to take advantage of this.
While this isn’t the kind of laptop that you’ll want to do some actual machine learning, it’s an excellent choice for you to start learning how to do it.
5. ASUS VivoBook 15
- 15.6 inch Full HD (1920x1080) 4-way...
- Compatible with Google Classroom; run...
- Latest 10th Gen Intel Core i3-1005G1 CPU...
- 8 GB DDR4 RAM and 128 GB PCIe NVMe M.2...
- Ergonomic backlit keyboard with...
Similar to the HP Stream 11, the Asus VivoBook 15 is better suited for learning machine learning than actually doing machine learning. However, it packs far more value at its price point and looks more expensive than it lets on.
Performance isn’t the strong suit of the Asus VivoBook 15 but it shouldn’t lag behind that much either.
The 10th generation Intel Core i3-1005G1 is a 2-core, 4-thread processor. It’s then paired with an 8GB DDR4 RAM with a 128 GB PCIe NVMe M.2 SSD.
Altogether, the laptop should have enough processing power for most of your daily tasks and cloud computing.
Despite having a 15.6-inch Full HD IPS display, the VivoBook 15 manages to keep itself thin with measurements of 14.10 x 9.10 x 0.78 inches and, perhaps more importantly, lightweight at just 3.75 pounds.
The high-quality lithium-ion battery isn’t good. It should last for around 4-5 hours, which is short considering that you’re not powering a graphics card.
If it’s any consolation, the battery charges relatively quickly.
With an 88% screen-to-body ratio and a four-sided NanoEdge display for its 15.6-inch IPS screen, the VivoBook 15 looks the part of an expensive laptop despite having a relatively low price tag.
The display is powered by Intel UHD graphics.
Even though the VivoBook 15 has an ultra-slim bezel, it still manages to squeeze in an HD camera.
The VivoBook 15 is also compatible with Google Classroom. It comes with an ergonomic backlit keyboard, a fingerprint sensor that you can activate via Windows Hello, as well as what Asus refers to as an Ergolift design that helps make typing more comfortable.
At this price point, you’ll be doing more learning and coding than actual machine learning, which the VivoBook 15 is perfect for.
Ideally, you’d want a 15-inch display to see the source code better, but at 15.6-inches with thin bezels, it’ll do. Not to mention, it boots fast thanks to its SSD. Although it could have used a longer battery life, you can’t really ask for more at the price that it’s selling for.
6. Dell XPS 15 7590
“Best for Touchscreen Display”
- InfinityEdge display: The virtually...
- CinemaColor Visuals appear every bit as...
- Leading-edge connectivity Thunderbolt 3...
- Revolutionary webcam construction: The...
- Ports & Slots: 1. SD card slot | 2. USB...
The Dell XPS 15 7590 won’t be able to handle heavy long-term graphics-intensive and processing loads due to its form factor and undersized thermals.
Still, it earns a spot because it has an unusually powerful processor (for a thin laptop anyway) and a passable graphics card that should let you toy around with machine learning on the go without having to sit down on a desk.
Earlier, we pointed out the unusually powerful processor of the Dell XPS 15.
The main reason for this is because it’s powered by a 9th generation Intel Core i7-9750H, which boasts one of the fastest single-threaded performances on a consumer-grade laptop before being replaced by the newer i7-10750H.
The processor is then mated to a 16GB DDR4 RAM with a 512GB M.2 PCIe NVMe SSD.
Portability is the key selling point of the Dell XPS 15.
It’s barely half an inch thick with measurements of 9.70 x 14.06 x 0.66 inches and it weighs only 4 pounds.
The laptop also comes with a battery gauge button and indicator with a battery life that can last for as long as 10 hours when doing light tasks like browsing or as low as around 5 hours for when doing heavier applications.
The Dell XPS 15 has arguably the most beautiful display on our list if not the brightest.
The 15.6-inch 4K UHD InfinityEdge IPS display is anti-reflective with 100% AdobeRGB and 500-nit brightness.
The display itself is very close to being considered bezel-less. This allows the laptop to maximize every inch of space in its compact body, allowing it to fit a 15.6-inch screen in a body of a laptop that’s closer in dimensions to a 14-inch laptop.
The glass is also made out of Corning Gorilla Glass 4, which is extremely handy for when you prefer to use the touchscreen functionality.
The display is then paired with a relatively modest and power-efficient graphics card in the Nvidia GTX 1650 with 4GB of GDDR5 VRAM.
One good thing about the Dell XPS 15 is that it has a Thunderbolt 3 port.
The port comes in handy in a lot of ways. You can use it to charge your laptop, connect it to multiple devices, or enjoy the fast data transfer speeds. However, the biggest advantage that it has for machine learning is that you can use it to plug in an external GPU.
Because the Intel i7-9750H is a powerful processor, you won’t have to worry about it bottleneck the performance of the graphics card.
As a result, you can pair it with something like the RTX 2080Ti for better on-the-go machine learning performance.
The best thing about the XPS 15 is that, on its own, it’s an excellent laptop for machine learning on the go with a powerful processor capable of handling resource-intensive algorithms and large data sets.
Not to mention, it has an excellent 4K touchscreen display with virtually zero distracting bezels that make looking at lines upon lines of source codes less strenuous on the eyes.
However, it is the presence of a Thunderbolt 3 port that drives up the value of the XP 15.
Having a Thunderbolt 3 port means that you can enjoy plugging in an external GPU and make yourself quite the powerful portable set-up for deep learning.
7. Lenovo ThinkPad P15
“Best Desktop Replacement”
- Intel 10th Generation Octa-Core...
- 15.6" FHD (1920x1080) IPS Screen with...
- 128GB DDR4 Memory
- 4TB PCIe NVMe M.2 Solid State Drive (Two...
- Windows 10 Professional (Win 10 Pro...
Replacing a desktop for machine learning is no easy task. A laptop needs to be powerful and have sufficient cooling to do it.
The Lenovo ThinkPad P15 Gen 1 has both, and then some.
What makes the ThinkPad P15 is its workstation-grade components.
This workstation laptop isn’t just a replacement for a proper desktop. In some aspects, it’s even better than some desktops. It has a 10th generation Intel Core i7-10875H, which has 8 cores and 16 threads and can run up to 5.1Ghz with Intel Turbo.
The laptop also comes with a whopping 128GB DDR4 RAM, so handling multiple heavy workloads at once is easy for it.
Lastly, with 4TB worth of NVMe SSD storage, the laptop won’t just load fast. The laptop will also have far more storage than most people will ever know what to do with.
In terms of dimensions and weight, you’d understand if the ThinkPad P15 is not easy to lug around.
At 6 pounds, it isn’t. Yet, it’s not as heavy as you’d expect for a laptop that has MIL-STD-810G military certification. Not especially when it’s able to still be right about an inch thick at 14.78″ x 9.93″ x 0.96″.
Naturally, the battery life isn’t good.
Sure, the 94Wh battery is larger than what you’d find in most laptops. However, you have to keep in mind, the laptop isn’t exactly housing low-powered components.
An underrated feature of the ThinkPad 15 is its 15.6-inch Full HD IPS display.
The said display has Dolby Vision HDR400 with a 1,200:1 contrast, and a 500-nit brightness, which puts it right on par with the Dell XPS 15.
Of course, the bread and butter of the ThinkPad 15 is its workstation-grade graphics card.
The NVIDIA Quadro T2000 only has 4GB VRAM but it’s specifically designed for professional use. This includes machine learning, or to be more specific, deep learning or neural network computing.
The ThinkPad 15 was built tough, having passed the MIL-STD-810G military certification. This means that it can survive multiple falls of up to 4 feet with no problem.
The compressed keyboard design can also take a bit of time getting used to. This is purposely designed so that it can fit a number pad, so you don’t necessarily get a full-sized keyboard but you do get all of the keys along with it.
Speaking of the keyboard, once you get used to it, it sports the signature Lenovo ThinkPad comfort and is backlit too.
At this price point, it’s worth considering buying a more portable but powerful laptop and pouring the rest of your cash into an actual workstation that you can use at home.
Still, there’s more merit to be had for a set-up that can do both.
While the ThinkPad 15 won’t be spending much time away from the cord, you can carry it around and move it from one office to another.
As such, we see the ThinkPad 15 as the ideal companion for machine learning professionals who don’t want to compromise performance and portability.
8. Microsoft Surface 3
“Best for Portability”
- PLEASE NOTE: This LAPTOP IS MADE FOR...
- Quad-core 10th Gen Intel Core i7-1065G7...
- Windows 10 Pro
- Intel Iris Plus Graphics
- 1 x USB-C 1 x USB-A 3.5 mm headphone...
For presenting machine learning models and compiling code on the go, the Microsoft Surface Laptop 3 is an excellent choice.
The Microsoft Surface Laptop 3 is one of the few laptops on the market that uses G series processors, which aims to maintain excellent single-threaded performance while pairing it with above-average graphical performance.
The Intel i7-1065G7 doesn’t have 8 cores and 16 threads unlike the i7-10750H, but with 4 cores and 8 threads, it has more than enough performance for programming and coding tasks.
It also helps that the laptop has 16GB of DDR4 RAM with a 512GB NVMe SSD.
What the Surface Laptop 3 sacrifices in high-end performance, it makes up for in portability.
At just 2.65 pounds, the laptop weighs nearly just as much as the HP Stream 11 despite having a full-sized 15-inch display.
The laptop also has an estimated battery life of 11.5 hours with fast-charging capabilities. The latter is extremely important. This is because it lets you power the laptop and charge it up to 80% from 0% in just an hour.
The Surface Laptop 3 has a unique resolution of 2496 x 1664 (with a 3:2 aspect ratio) for its 15-inch PixelSense Display that is far more efficient at reading lines upon lines of codes among others.
The display is also surface pen-enabled with 10 point multi-touch functionality.
Powering the 15-inch display is the Intel Iris Plus graphics, which really won’t do anything for your models.
What this means is that most of your data and iterations are dependent on CPU performance.
Perhaps the main selling point of the Surface Laptop 3 for programmers is its keyboard and touchpad.
The keyboard is an absolute joy to type on. It has just the right amount of space between the keys and doesn’t feel mushy nor too sturdy. The 1.3mm travel is generous, to say the least, and each key press feels satisfying.
The touch-enabled display is also a nice novelty feature, but if you’re programming and coding, you prefer to move your cursor around.
While the Surface Laptop 3 is admittedly small in size, it’s responsive and gives off a mouse-like thud whenever you click on something.
A nice screen, comfortable keyboard, and long battery life can go a long way in coding as it helps minimize any potential distractions.
The Surface Laptop 3 has all three of that and it’s lightweight, as well as thin.
Although the lack of Thunderbolt 3 means that you can’t pair it with an external GPU down the line, we still wouldn’t recommend it anyway even if that was possible.
9. Apple MacBook Pro (2020)
“Best for Programmers”
- 16" MacBook Pro with Touch Bar - 96W...
- Stunning 16" Retina display with True...
- Touch Bar and Touch ID
- Ultrafast SSD
- Six-speaker system with force-cancelling...
While machine learning isn’t all about programming and coding, it’s still a huge part of it.
This is why many programmers prefer to use a Macbook for coding because it has better cross-platform compatibility and it’s a fully-featured Unix operating system. This is important because it lets you run programs in virtually every language without the need for specialized IDEs.
With that said, the Apple 16” MacBook Pro is as good as a portable MacOS device gets before you reach the absurdly priced offerings.
Despite most of the benefits of using a MacBook Pro to code in machine learning coming from the macOS itself, this laptop isn’t too shabby itself.
It’s powered by a 9th Generation Intel Core i9 processor running at 2.4Ghz at stock and capable of reaching up to 5.0Ghz with turbo boost.
You’ll also have 64GB of RAM available. This is more than enough for model trains and deep learning.
Finally, the 1TB proprietary SSD drive ensures fast boot times with plenty of storage space.
MacBook Pro’s have always done well when it comes to portability because of their sleek and compact sizes, as well as large battery lives.
This one is no exception.
Despite sporting a 16-inch screen, the 100 watt-hour battery is rated for up to 11 hours of wireless web browsing. This should translate well to coding, albeit expect it to drop drastically when used for more intensive tasks.
Perhaps more importantly, it’s able to maintain dimensions of 14.09 x 9.68 x 0.64 inches with a 4.3-pound weight.
The 16-inch Retina display sports a unique 3072 x 1920 resolution with 500-nit brightness, as well as P3 wide color and True Tone technology.
Those are terms that you most likely will not care about but your eyes will appreciate well.
In addition to this, the display is powered by the AMD Radeon Pro 5500M with 8GB of VRAM.
In terms of performance, the graphics card should fare better than the Nvidia GTX 1650 n gaming and machine learning. However, it won’t benefit from the CUDA cores for the latter purposes.
After years of complaints from the butterfly-switch keyboards they previously used, Apple has finally switched them out for the substantially better keyboard on the latest 16-inch Apple MacBook Pro.
The design is supposedly based on the Magic Keyboard used in iMacs.
They’re not particularly springy, but the full millimeter of travel gives it a satisfying but quiet thunk that you won’t quite find anywhere else.
The design and layout is also much more convenient to use this time around.
We especially liked how far more spaced apart the keys are. We simply love how you won’t accidentally touch the Touch Bar anymore.
In addition to this, the laptop has four Thunderbolt 3 ports that you can use.
It’s compatible with the Blackmagic eGPU, which features up to a Radeon Pro 580 GPU.
Not only can this help save the thermals of your MacBook Pro, but the addition of the GPU helps make most of your small and medium Deep Learning model training much faster and more comfortable.
Heavy machine learning processes will often force you to get a more static desktop solution.
The Apple 16″ MacBook Pro is an excellent compromise. It effectively lets you train deep neural nets using the eGPU, and for lighter loads, the onboard graphics card is more than adequate, to say the least.
10. OMEN by HP 15″
“Best Overall (Alternative)”
- Hyper-realistic graphics: NVIDIA(R)...
- OMEN command center: Easily turn up your...
- Cutting-edge gaming performance: Play...
- Super-fast processor: 9th Generation...
- Memory: 16 GB DDR4-2666 SDRAM...
A more expensive alternative to our top choice is the OMEN by HP 2019 15-inch Gaming Laptop.
Although the hardware is compelling, it’s not necessarily better. Not to mention, it doesn’t solve the same thermal problem that plagues that of our top choice, which explains why we chose it as an alternative.
Despite being a generation older, the Intel Core i7-9750H found inside the Omen 15 is still a beast of a processor.
It has a base frequency of 2.6GHz and can reach up to 4.5Ghz with Turbo Boost. It also has 16GB of DDR4 RAM that can be upgraded up to 32GB. It also comes with a 512GB PCIe NVMe M.2 SSD for speedy boot times and faster data loading or transfers.
With dimensions of 14.20 x 10.40 x 1.00 inches and weighing 5.1 pounds, portable isn’t exactly a word we’d used to describe the Omen 15.
HP claims that the battery of the Omen 15 can last for up to 13 hours with mixed light usage and nearly 7 hours of pure wireless streaming.
This claim is proven by numerous users who were able to use the laptop for a while before needing to plug it in. However, being that this is a gaming laptop, we suggest bringing the charger with you anyway.
Coding sessions can run long, and paired with machine learning processes, it’s always best to stay safe.
The bezels on the 15.6-inch FHD IPS display are noticeably thinner than what you’d find on other gaming laptops.
The display quality isn’t excellent, but it’s above average. It should be bright enough to be used outdoors. Although judging by how chunky it is, we suggest using it on top of a desk to save your legs or lap some trouble.
The display is powered by a Max-Q version of the NVIDIA GeForce RTX 2070 with 8GB of GDDR6 VRAM.
The full-size island-style keyboard comes with 4-zone RGB lighting and has an average key travel of 1.5mm.
It’s nothing special, but it’s comfortable. At the very least, it won’t be causing you any pain or distractions as you focus on coding or programming.
You also have a smooth touch trackpad that’s conveniently located just below the keyboard with ample amounts of space so that you don’t have to worry about pressing it by mistake.
Lastly, the extensive range of I/O ports comes with 1 x USB 3.1 Gen 2 Type-C with Thunderbolt 3, a USB 3.1 Gen 1 Type-A, 2 x USB 3.1 Gen 1 Type-A, as well as a mini display port, an HDMI out, and even an SDcard reader, among others.
The Omen 15 is a great gaming laptop with just the right kind of hardware for machine learning professionals looking for something that they can use on the go without having to need to spend money on a workstation-grade laptop.
Specs requirements for machine learning laptop
If you want to buy the best laptop for machine learning, you need to know what you’re looking for and why that’s needed.
Here are the most parts to consider when shopping for a laptop for machine learning.
Memory / RAM
Training machine learning models can be especially taxing on your system. Not to mention, this doesn’t include other tasks and processes you may already have running in the background of your laptop or computer.
Because of this, we recommend 8GB for learners and starters. However, at this point, you should expect to see some performance hiccups.
Upgrading to 16GB helps solve some of these concerns.
Ideally, you should be set if your laptop has 32GB of RAM.
Processor / CPU
The processor isn’t anywhere near as important for machine learning. Most of the time, its main purpose is to initiate GPU functional calls and occasionally execute CPU-related functions like data preprocessing.
Because of this, having a powerful processor isn’t as important in machine learning, but it does help.
To maximize GPU performance, it’s always best to have more cores and threads.
The main reason for this is because it lets you process data so you can feed it to your graphics card at a much faster rate.
If we’re being more specific, a 9th/10th Gen Intel i7 Processor (6 cores, 12 threads) or an AMD Ryzen 7 (8 cores, 16 threads) should be safe choices.
These choices give you headroom over the 4-core minimum for GPU-intensive machine learning tasks. At the same time, they’re plenty powerful if you plan on using your processor for machine learning.
Graphics Card / GPU
The graphics card is the heart of deep learning. It’s far too important to cheap out on.
One important thing to note is that Nvidia’s graphics cards are the top-dog for machine learning.
The main reason for this is that they’ve had extensive support for the software for a while now, including drivers, CUDA, and cuDNN.
Although this doesn’t necessarily make AMD graphics cards obsolete for machine learning, you’ll get more performance out of your money if you went with a laptop with an Nvidia graphics card.
In particular, the NVIDIA GeForce RTX 2070 is considered the most cost-effective GPU laptop solution for machine learning.
Although the RTX 2070 Super, RTX 2080, and RTX 2080 Super are good choices as well, the price difference between laptops with the said graphics cards and the RTX 2070 is far too large to ignore.
If you’re spending more money, you’re better off getting a laptop with an RTX 2080 Ti.
With 11GB of VRAM, the RTX 2080Ti can handle anything from hunting state-of-the-art scores to training models.
Even with just 8GB of VRAM though, the RTX 2070 can handle most machine learning tasks.
For a more budget-friendly alternative, the GTX 1660Ti isn’t half-bad with 6GB of VRAM. However, you’ll be dropping the benefit of having an RTX graphics card.
In terms of machine learning, the difference between using an SSD or HDD isn’t as noticeable.
The main difference is when you’re using it for day-to-day tasks.
Even just from booting up your laptop alone, the performance gap is substantial enough that you’ll never want to go back to using an HDD outside of extra storage again.
To have enough space for your OS and other software, a laptop should have at least 256GB worth of SSD storage space.
Software packages like JMP, Weka, and RapidMiner work well with Windows when performing more basic machine learning operations.
In general, though, most machine learning professionals prefer to use a Linux-based OS.
There’s also a sizable number of those in the machine learning industry who enjoy the advantages of coding and programming on a Unix-based OS like macOS.
Frequently asked questions
Which Laptop Is Best for Machine Learning?
The MSI GL65 Leopard 10SFK-062 is our top pick as the best laptop for machine learning.
The laptop has a fairly powerful 6-core and 12-thread processor with 16GB of RAM (upgradable to 64GB) and 512GB of NVMe SSD storage space. However, what sells it is its value and GPU.
You’ll find it difficult if not impossible to find a laptop at the same price point with an Nvidia GeForce RTX 2070 GPU.
Is MacBook Good for Machine Learning?
Apple seems intent on making sure that its MacBook line of laptops are good for machine learning.
Case in point, the recently-released Macbook Pro (13” and 15”) and MacBook Air with Apple M1 chips feature what Apple refers to as the “Apple Neural Engine”.
Apple claims that this technology can execute somewhere around 25,000 threads all at the same time. More importantly, the 16-core Neural Engine is reportedly capable of executing 11 trillion operations every second.
This is still mostly just for running machine learning inferences and not for actual machine learning training.
Still, many developers seem to agree that this is a step in the right direction.
As for the 16-inch MacBook Pro laptops, they still come with more “conventional” Intel Core i7/i9 processors with up to 64GB of memory.
They’re great if you know what you’re doing. The display is beautiful and the improved keyboard is nice. It’s also one of the few laptops that’s able to keep the beastly i9 at reasonable temperatures.
The only issue is that it comes with an AMD graphics card (AMD Radeon Pro 5500M) and the eGPU solution has an AMD graphics card as well (AMD Radeon Pro 580).
Is Ryzen Good for Machine Learning?
As the saying goes, it’s best to have something when you don’t need it than not to have something when you do need it.
Ryzen mobile processors, or to be more specific, the Ryzen 7, comes with 8 cores and 16 threads. The architecture is also known for its efficiency and a noticeable edge in multithreaded performance over its Intel counterpart.
A Ryzen 7 laptop will let you train pre-processed features with ease. At the same time, it’ll be just as capable for other more CPU-intensive machine learning projects.
Can I Use AMD GPU for Machine Learning?
Yes. You can. We just wouldn’t recommend it.
One of the biggest issues with AMD graphics cards in machine learning, or just their graphics cards in general, is their software.
There is a reason why AMD graphics cards are said to “age like fine wine”.
This is because it takes AMD quite a while to optimize their cards and make sure that they can be utilized properly by gaming titles.
The same goes for machine learning, where, even though their hardware is perfectly cut out for deep learning, the lack of software support and maturity makes them a no-go for professional applications.
If you already have a laptop with an AMD GPU, you’re still free to try our machine learning with it.
If, however, you’re planning to buy a new laptop for machine learning, you’re better off with a laptop with an Nvidia graphics card.
The Final Verdict
Buying the best machine learning laptop is a good way to improve your productivity in your field.
Of course, it should already go without saying, but you’ll be getting far more for your money if you built a workstation-grade desktop instead.
Another way to circumvent the need for a powerful laptop for machine learning is through cloud computing.
In the end, the choice is up to you.
If you prefer to have the best machine learning laptop that you can use today, we’re hoping that our list can help you out.
We made sure to make it as comprehensive as we can and to include recommendations for every potential user and their preferences, as well as their budget.
With that said, we are open to suggestions.
Feel free to let us know if you know of any laptop that might be better suited for machine learning than we suggested or if there’s anything that we might not have covered or missed.
Last update on 2021-04-15 / Affiliate links / Images from Amazon Product Advertising API