Now that machine learning is getting more and more popular, there is a growing need for faster and more efficient computation to train machine learning models.
Machine learning is the new trend in the programming world, and almost all of the new innovations revolve around artificial intelligence.
The only limitation to having a human-like program is training the algorithm; for the AI to make more accurate predictions, we need to feed more and more data to it. Usually, to gather such a large amount of data, AIs are left unsupervised on the internet, where they filter and feed on all the relevant information available.
As there is a lot of data available on the internet, to train the model faster we need higher computation power, or it will take ages for your AI to become relevant.
In this article, we’ll try to cover everything you need to know while buying a laptop specifically for machine learning, along with our best picks for the best laptops for machine learning, deep learning, and data science.
Ideally, you should use a desktop with a powerful GPU because it will be faster and easier to use. To give you an idea, a laptop GPU is different from a desktop GPU, even though the model name is thesame. A laptop GPU is smaller, uses less power, and is less powerful than a desktop GPU.
Laptops tend to be used for tasks such as coding, statistics, and data analysis. Laptops can be used for data science and machine learning tasks, but for heavy computations, laptops are not ideal.
Additionally, many people prefer to use cloud services such as Google Colab, RunPod, AWS SageMaker, and others, because in certain cases, it is cheaper and easier to use. These cloud services provide you with a powerful GPU and CPU for free or at a very affordable price.
However, laptops are portable and you can use them anywhere and you may have to travel with them.
If you’re someone who also wants a laptop with specs that can handle some light deep-learning training, then this article should be helpful. We’ve rounded up some of the best laptops for data science and deep learning, so you can find the right one for your needs.
Table of Contents
- Best Laptops for Data Science & Deep Learning – Our Top 7 Picks
- Overview of Our Picks
- Factors to Consider when Choosing A Laptop for Machine Learning
Best Laptops for Data Science & Deep Learning – Our Top 7 Picks
|Lambda TensorBook at LambdaLabs||GPU: RTX 3080 Super Max-Q (8 GB of VRAM).|
CPU: Intel Core i7-10870H (14 cores, 20 threads)
RAM: 64 GB of DDR4 SDRAM.
Storage: 2 TB (1 TB NVMe SSD and 1 TB SATA SSD).
Display: 240 Hz, 1440p, 15.6 inch
|Buy on LambdaLabs.com|
|Apple Macbook Pro (M1 Max Chip)||GPU: 32-core GPU; 16-core Neural Engine; 400Gbps memory bandwidth|
CPU: 10-core CPU with eight performance cores and two efficiency cores (10 threads)
RAM: 32GB unified memory Configurable to 64GB
Storage: 1TB SSD Configurable to 2TB, 4TB, or 8TB
Display: 120 Hz, 16.2-inch (diagonal) Liquid Retina XDR display, 3456×2234 native resolution at 254 pixels per inch
|Buy on Amazon|
|GIGABYTE G5 Series||GPU: NVIDIA GeForce RTX 30 Series Laptop GPUs|
Processor: 11th Gen Intel Core i5 Processor H-Series (6 cores, 12 threads)
RAM: 2x DDR4 Slots (DDR4-3200, Max 64GB)
Storage: 512 GB NVMe SSD
Display: 144 Hz, 15.6 inches, 1920 x 1080
|Buy on Amazon|
|Apple Macbook Pro (M1 Pro Chip)||GPU: 16-core GPU; 16-core Neural Engine; 200GB/s memory bandwidth|
CPU: 10-core CPU with eight performance cores and two efficiency cores (10 threads)
RAM: 16GB unified memory, configurable to 32GB
Storage: 512GB SSD Configurable to 1TB, 2TB, 4TB, or 8TB
Display: 120Hz, 14.2-inch (diagonal) Liquid Retina XDR display; 3024×1964 native resolution at 254 pixels per inch XDR (Extreme Dynamic Range)
|Buy on Amazon|
|CUK ASUS Rog Strix Scar 15||GPU: NVIDIA GeForce RTX 3080 8GB GDDR6|
Processor: Intel Core i9-12900H (14 core, 20 threads)
RAM: 32GB DDR5 4800MHz (upgradable to 64GB)
Storage: 1TB NVMe Solid State Drive Gen4
Display: 15.6-inch WQHD 240Hz 3ms IPS-level Display (2560 x 1440)
|Buy on Amazon|
|Razer Blade 15 at Razer||GPU: Nvidia's RTX 3080 Ti|
CPU: Intel Core i9-12900H (14 core, 20 threads)
RAM: 32GB 4800MHz RAM
Storage: 1TB SSD (M.2 NVMe PCIe 4.0 x4)
Display: 144Hz UHD, 15.6 inches
|Buy on Razer.com|
|Dell Alienware X17 R2 at Dell||GPU: NVIDIA GeForce RTX 3080TI 16GB|
CPU: Intel Core i9 12900HK (14 cores, 20 threads)
RAM: 32 GB
Storage: 1 TB, PLCe NVMe SSD
Display: 17.3", FHD 1920x1080, 360Hz, Non-Touch, 1ms
|Buy on Dell.com|
Overview of Our Picks
The Lambda TensorBook is a laptop created exclusively for machine learning. It is a masterful fusion of software and hardware that results in a very amazing platform that gives ML developers plenty of capability for testing and development.
You might have never heard of Lambda as a company in the laptop industry, and you are correct. The TensorBook was created by Razer in collaboration with Lambda, a company that excels in deep learning.Our Take
Though this is a one-of-a-kind and first-generation machine learning laptop, if you can afford it, you should surely go for it as it is crafted with thoughtfulness about programmers and their needs. Also, Razer is a famous manufacturer of gaming peripherals, so you need not worry about laptop design. This laptop is especially curated for deep learning and is the only one of its kind.
- Deep learning software is already installed
- It’s especially designed for deep learning
- Amazing Specs
- 4x faster than Apple M1 Max
- Up to 10x faster than Google Colab instances
- It’s very expensive
- First laptop of Lambda-Razer collaboration, which means they may have to still work out the kinks
- Lack of reviews because it’s a new product, it’s expensive, and targeted at a very niche market
The MacBook Pro M1 Max is the most powerful laptop ever created by Apple. The M1 Max, along with the M1 Pro, is Apple’s second System on a Chip (SoC) developed for use in Macs.
System on a chip (SoC) means that the CPU, GPU, RAM, Neural Engine, encode/decode engines, Thunderbolt controller with USB 4 support, and a lot more functionalities are all integrated into the M1 Max as a “system on a chip” to enable the many functions of the Mac.
Traditional Intel-based Macs used multiple chips for the CPU, GPU, I/O, and security, but integrating multiple components onto one chip allows Apple silicon chips to operate faster and more efficiently than Intel chips.Our Take
The MacBook Pro M1 Max is a high-grade professional computer that is extremely fast, has a screen that is razor-sharp, and has superb build quality.
Apple is known for its product quality, so you won’t have to worry about system malfunctions or software issues as their products are the best in class available out there.
If you are looking for a balanced laptop with an amazing combination of best-in-class macOS and the M1 Max chip, this laptop can handle heavy-duty machine-learning algorithms easily.
- 16-core Neural Engine for machine learning, which means it can handle more complex tasks
- Integrated Thunderbolt 4 controllers that offer more I/O bandwidth than before
- Beautiful Liquid Retina XDR display
- Incredible performance
- Amazing speakers
- Display not 4K
- Configuration upticks are expensive, meaning that the price of this laptop can quickly become quite high
- Obtrusive camera notch
- Quite heavy
Gigabyte is a Taiwanese computer company that designs and manufactures motherboards for both AMD and Intel platforms, and also produces graphics cards and notebooks in partnership with AMD and Nvidia.
The company is well known for its high-end products. Gigabyte primarily targets gamers and content creators. These machines have a simple, no-frills design and are strong and reliable.
These are the only budget laptops that you’ll see on this list, as other laptops, even some that are $1000 more expensive than this one, cannot overpower the free Google Colab.Our Take
If your budget is not super high and you want an amazing value-for-money laptop for machine learning, then one of these is probably the best you can get.
The laptops are very reasonably priced and surpass all of their competitors by a pretty good margin.
- Surprisingly strong display
- Punchy 105W Nvidia RTX 30 series GPU
- Good value
- Uses an old, if powerful, CPU
- Poor battery life
- Keyboard gets a bit toasty in parts due to limited thermal isolation
The MacBook Pro M1 Pro is a slightly more budget-friendly but less powerful version than the M1 Max.
The two laptops have almost similar specifications, with the difference being that the M1 Max is more powerful and has a better CPU and GPU along with higher RAM and storage capacity.Our Take
If you are looking for a balanced laptop at a pocket-friendly price that also supports the Apple ecosystem, then this is probably the best option for you.
Apple quality will never disappoint you, and you get good processing power that is enough for moderate-level machine learning and AI development.
- Beautiful Liquid Retina XDR display
- Incredible performance
- HDMI output and SD card reader
- A huge leap in graphics performance over earlier M1 systems
- Function keys replace the Touch Bar
- Configuration upticks are expensive
- Obtrusive camera notch
- Very heavy
ROG stands for Republic of Gamers, a brand created by ASUS for its top-of-the-line gaming systems. It is a brand of desktop and laptop hardware that is geared toward gamers.
The Strix Scar 15 (2022) is not the most powerful or the most expensive laptop from ROG, but it is an amazing combination of powerful hardware and reasonable cost.Our Take
The ROG laptops are designed to handle the extreme CPU and GPU requirements to run the latest high graphics games. They are also equipped with top of the line cooling systems, so you won’t have to worry about your hardware overheating, even if you are training an extremely heavy AI algorithm.
This is an amazing choice if your budget is under $3k.
- High-quality 1440p display
- Clicky optical-mechanical keyboard keys
- Superb application pace
- Bold, RGB-covered design
- Solid build quality
- Ultrafast SSD RAID0 array
- Turbo mode required for full 130 W GPU performance
- No webcam
- Some missing connectivity features
- Tends to run hot, but doesn’t throttle
- No fingerprint reader
Razer is one of the world’s leading lifestyle brands for gamers. In the worldwide gaming and eSports communities, the triple-headed snake trademark of Razer is one of the most recognizable logos.
Even though the Blade 15 is luxuriously priced, it comes with top-notch components, a strong frame, and impressive performance in a slick and (comparatively) subdued design. It is a fantastic replacement for large desktops.Our Take
Another gaming laptop makes it on this list as we all know how similar the computational requirements are for machine learning and high graphics games.
This should be your choice if you slightly lean towards gaming and need a better display as compared to the Tensorbook, as both of these are in the same price segment.
- Excellent build quality
- Lots of config/screen options
- Strong performance
- New 1080p webcam
- Great port selection
- High starting price
- Just OK battery life
- Proprietary power plugs
The Alienware laptop series is well-known for its high performance, striking design, and sleek style. Its high cost makes it a preferred choice for gamers, as they offer the most powerful hardware available. Alienware uses high-end processors like the latest Core i7 and i9 to deliver the best performance.
The Dell Alienware X17 R2 is a beast of a laptop with the best elements stacked in it.
Its amazing power is fueled by a 12th-generation Intel Alderlake i9-12900H processor that conveys remarkable performance.Our Take
The Alienware X17 R2 is the most expensive laptop on this list. This laptop is a beast with all the latest and best hardware inside it. It is essentially a gaming laptop, but its computational powers are no joke. This is probably the most powerful laptop on this list in terms of computational power required for machine learning.
Also, Dell is a trustworthy brand and focuses on quality, so if you have an insanely high budget, then this laptop is surely a treat.
- Slim yet powerful
- Amazing cooling(With individual fan control)
- XMP and overclocking with Alienware control center
- Very expensive
Factors to Consider when Choosing A Laptop for Machine Learning
RAM (random access memory) is a computer’s short-term memory, where the data that the processor is currently using is stored. Each task needs some information that is stored in RAM, so if you have less RAM and you try to perform multiple tasks at once, you will run out of space in RAM, so tasks will be put on hold, and in some cases, some of the tasks of the operating system of your device will be put on hold, which may cause lag or, in some cases, even a system crash.
Your OS already uses about 3GB of your RAM, and moreover, you will often need to install virtual operating systems on your laptop for big data analytics. Such virtual operating systems need at least 4 GB of RAM, in such cases, if your RAM is low, your device may start lagging and crashing
For deep learning, the minimum RAM requirement is 16GB, anything below that and you will face difficulties while multitasking. Keep in mind that increasing the RAM size does not speed up your computer. Higher RAM sizes will allow you to multitask.
GPU (Graphics Card)
A GPU is a specialized processing unit with enhanced mathematical computation capability, making it ideal for machine learning.
Unlike the CPUs, GPUs work on the SIMD (single instruction, multiple data) architecture. As the name suggests, it is used to perform a single operation on different data. A GPU is built from the ground up to almost exclusively render high-resolution graphics and images, a task that doesn’t involve a lot of context switching. Instead, GPUs emphasize concurrency, which is the division of large jobs into smaller ones that can be executed concurrently, such as the similar calculations needed to provide effects for lighting, shading, and textures.
You also might have heard of cores in GPUs; a core performs primarily one of two possible functions: it’s either executing code or computing. Unlike a CPU core, a GPU core does not decode instructions, nor does it access VRAM. But it computes in parallel—so more cores mean more parallel computational power.
NVIDIA and AMD are the two major brands of graphics cards.
TensorFlow’s deep learning library uses the CUDA processor, which compiles only on NVIDIA graphics cards. I highly recommend you buy a laptop with an NVIDIA GPU if you’re planning to do deep learning tasks.
Modern Nvidia GPUs come with three different types of processing cores:
- CUDA Cores: Similar to how your CPU could have two or four cores, CUDA Cores are parallel processors, and Nvidia GPUs can have hundreds or thousands of them. The cores are in charge of processing all the data going into and coming out of the GPU and working on the game graphics calculations that the user sees.
- Tensor Cores: NVIDIA Tensor Cores perform multi-precision computing for efficient AI inference. Turing Tensor Cores provide a range of precisions for deep learning training and inference. They basically perform mathematical operations called tensor maths
- Ray-Tracing Cores: These don’t have a part to play in machine learning. RT Cores are accelerator units that are dedicated to performing ray-tracing operations with extraordinary efficiency. Combined with NVIDIA RTX software, RT Cores enable artists to use ray-traced rendering to create photorealistic objects and environments with physically accurate lighting.
A GTX 1650 or higher GPU is recommended. The GTX 1650 is a GPU that features 896 CUDA cores, but it doesn’t have any tensor cores. The GTX 1650 was launched 3 years later than 1080 but was inferior to the GTX 1080 in many aspects one of them being the number of cores (2560 CUDA cores and 240 tensor cores) but the GTX 1650 is 3 times cheaper than GTX1080.
If we look at it sequentially, you can get any of the NVIDIA series greater than the GTX 1080 or anything that was launched after the GTX 1080.
The GTX 1650 is the cheapest GPU that will get your work done. It offers the best value for money.
We recommend you get a laptop with an NVIDIA RTX 3000 series GPU.
VRAM (Video RAM)
It is basically the size of your GPU, i.e. the GPU memory, also called video memory. It works just like your normal RAM; the only difference is that this memory is not used for normal day-to-day tasks but for heavy computation-demanding tasks like gaming, video editing, machine learning, etc. There is a need for very large batch sizes when training massive models on massive datasets. Therefore, more VRAM can aid in training models in larger batches, which will certainly shorten the training period for the models.
For Example: If you’re generally doing NLP (dealing with text data), you don’t need that much VRAM as the function of VRAM is to store either processed or soon-to-be-processed data, and text doesn’t occupy a lot of space. But if you’re generally dealing with CV (image data), you’ll usually need quite a lot of VRAM as images have a lot of information in them, and to store all this information, you’ll need a lot of space. The optimum video memory would be 8GB but a minimum of 4GB is required
Machine learning can be used to collect, analyze, and interpret large amounts of data and use it to perform a task. Some of these tasks can be accomplished through simple automation or more complex algorithms that generate various types of content with the data.
CPUs can be beneficial in these processes by providing quicker responses for memory transfer and quickly storing and retrieving data.
You want as powerful a GPU as you can get, but your CPU just needs to be good enough to continually feed the GPU data, or you will face a bottleneck situation where your GPU will not be able to work at its full potential.
Let’s say you are training your AI on images and you have a large number of images stored as files in folders. Then there’s additional processing that needs to happen in how the OS is needed to access the filesystem to pull up a .jpg and then decompress/process it into a normalized 3D array. That’ll take a bit, and the faster your storage and the more threads you can have preparing batches, the better. So while dealing with large datasets, CPUs are crucial for faster computation.
Newer-generation processors should always be preferred. Intel has launched its 12th generation and AMD has launched its 7th generation processors, so these are the absolute best you can get right now. You can still go for some lower-generation processors, but the processing power, new hardware compatibility, power efficiency, and thermal management drastically increase with newer-generation processors.
Minimum 7th gen of intel i7 processors or AMD Ryzen 5 series will be required for smooth functioning of your device.
You should also consider the number of cores and threads:
- Cores are the number of independent CPUs in a single chip, and threads are instructions that can be processed by a single CPU core.
- Cores and threads are the most critical requirements that you should consider as most of the machine learning tasks require parallel computations.
You should always consider buying a laptop with a higher number of CPU cores and threads.
We recommend at least 6-cores – 12-threads processors
Disk Space and Type
Traditional laptops come with HDDs (Hard Disk Drives). HDDs are really slow as they have to move mechanical parts, which delay the processing of information and reduce reliability and durability.
These are generally used for your normal computer storage, anywhere between 1TB-2B will be enough for your projects and datasets, depending on your requirements.
SSD (Solid State Drive)
It is a faster version of storage. SSDs are 10 times faster in terms of reading speed and 20 times faster in terms of writing speed as compared to conventional HDDs. SSDs are significantly more powerful than HDDs as they have no moving parts and provide superior performance.
We will always prefer an NVMe SSD over a normal SSD, as NVMe SSD is an upgraded version of a normal SSD. They are 6 times faster than a normal SSD.
SSDs are usually used in combination with HDDs, as SSDs are 2.5 times more expensive than HDDs. SSDs are typically used to install an operating system in them that improves your laptop’s speed exponentially.
If your deep learning workstation is going to be used to track images or videos, then you will need to store multiple images or videos (temporarily in most cases), and you need to be able to store and fetch your data as fast as possible. So the speed of your computation directly depends on how fast your memory drive is. Also, if you run out of SSD storage while your algorithm is running, it will have to store data on HDD (if available), which will slow down the process.
A minimum of 256GB of SSD will be necessary for your operating system and primary programs. But if you desire faster computations while training our algorithm, then a 512GB SSD is recommended so that you can store and run your algorithm on the SSD. You also need to ensure that you have enough space for all the data that the AI will collect.
External GPUs (eGPU) using Thunderbolt Ports
External GPUs are exactly what you just thought they would be; these are graphics cards that operate/exist outside of the laptop. Once set up, these can be used as plug-and-play with your laptop. External GPUs are used to achieve higher processing/rendering power to perform high computation tasks.
Thunderbolt ports are used in eGPUs as these ports provide high bandwidth for connecting high-speed devices. For smooth functioning of your task, the eGPU needs to be in sync with your system GPU, so the external GPU needs to be able to send and receive information at an extremely high speed, and to achieve this speed we need faster connection ports, i.e., Thunderbolt ports.
If you are using your laptop for machine learning, then an eGPU will be an amazing investment as it will improve your system’s performance, thereby saving you a bunch of time, and also be an important factor in maintaining your laptop’s health.
Can I perform deep learning on a system that does not meet the minimum hardware requirements?
Modern deep learning algorithms are computationally intensive, requiring a powerful central processing unit (CPU) and graphics processing unit (GPU) for training and inference. While it is possible to run deep learning on a system that does not meet the minimum hardware requirements, it is likely that the system will be significantly slower than a system that does meet the requirements.
However, now we have a lot of companies offering cloud-based computation services, some of them with free tiers like Google Colab or Kaggle.
Why does machine learning require so much computational power?
Machine learning algorithms require a lot of processing power in order to find patterns in data. They do this by looking at a lot of data points and making calculations on that data. The more data points there are, the more processing power is required.
Are machine learning and deep learning the same thing?
No, machine learning and deep learning are not the same thing. Machine learning is a method of teaching computers to learn from data without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks to learn from data in a way that mimics the way the human brain learns.
Dedicated machine learning laptops are yet to hit the market. Gaming and video editing and animation specific laptops are the closest you’ll get to an actual machine-learning laptop.
The gaming and animation industries are blooming, and new games come with higher graphics, and new animations (movies) require insanely higher computational requirements, similar to those required in machine learning.
That’s why on this list you’ll find only 1 dedicated machine learning laptop, i.e., the Lambda Tensorbook; apart from that, we have two Macbooks, which are amazing for video editing; and then all the remaining are gaming laptops.
So depending on your requirements and budget, you may choose any of the laptops suggested in this article.